K. N. Toosi University of Technology
This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.
Recently, there has been a great interest in the application of Lyapunov exponents for calculation of chaos levels in dynamical systems. Accordingly, this study aims at presenting two new methods for utilizing Lyapunov exponents to evaluate the spatiotemporal chaos in various images. Further, early detection of cancerous tumors could be obtained by measuring the chaotic indices in biomedical images. Unlike the available systems described by partial differential equations, the proposed method employs a number of interactive dynamic variables for image modeling. Since the Lyapunov exponents cannot be applied to such systems, the image model should be modified. The mean Lyapunov exponent is defined as a chaotic index for measuring the contour borders irregularities in images to detect benign or malignant tumors. Moreover, a two-dimensional mean Lyapunov exponent is incorporated to identify irregularities existing in each axis of the targeted images. Experiments on a set of region of interest in breast mammogram images yielded a sensitivity of 95 % and a specificity of 97.3 % and verified the remarkable precision of the proposed methods in classifying of breast lesions obtained from breast mammogram images.
Nowadays, Human Immunodeficiency Virus (HIV) does not have a certain cure and current treatment can only control the virus. In recent years, highly active antiretroviral therapy (HAART) is used for the treatment. Since HAART has also undesirable side effects, there is a trade-off in its dosage. To control the illness and minimize the side effects, a multi-objective problem can be solved for a treatment plan. Therefore, this paper presents multi-objective treatment strategies for HIV. Two cost functions are defined. One for drug dosage treatment and one for the concentration of CD4. The multi-objective problem solved by NSGA-II and NSIWO, to produce optimal control inputs. The Pareto frontier suggested optimal strategies which the regime is selected depending on the circumstance. The performance of the NSIWO and NSGA-II to find the Pareto front for this multi-objective problem is investigated.
This paper investigates the condition of polyethylene (PE) pipelines as a case study. This study introduces a novel method to detect and diagnose defects of high-density polyethylene (HDPE) pipes. The pipe defect detector technique (PDDT) is designed to capture and process the images from the inner surface of pipes. Consequently, PDDT is one of the nondestructive ways to investigate possible defects in pipes. The PDDT’s outcome offers valuable information regarding the shape, orientation, and length of defects in the inner surface of the pipe. This information plays an important role in defining the lifetime of the pipe and fault prediction. In this paper, a database consisting of a total 350 images was used to train, test, and verify a neural network system. For this purpose, input image quality was enhanced by applying Gabor and entropy filters. Then, the trained neural network was used to classify the input images into five defect categories. These categories are defined in a way to describe the shape and the orientation of the defects. Afterward, a curve completion method (CMM) that effectively derives the defect dimensions such as diameter and length was introduced. Finally, the life prediction methods that can use PDDT’s result to predict the time that actual fault may occur in the pipe are discussed.
Fault detection in non‐linear system has drawn a lot of attention recently. A typical solution is the generalization of linear methods to include non‐linear dynamics. This study addresses fault detection in non‐linear systems by extending parity relations using Takagi‐Sugeno (T.S) fuzzy models. Parity equations for linear systems are a residual generation method that has appealing capabilities in fault detection. T.S fuzzy systems are also extensively used in modelling of non‐linear systems. In this paper, parity equations are rewritten in the form of non‐linear systems that can be modelled by T.S fuzzy system. An advantage of this approach is that parity vector can be derived from relations explicitly. An algorithm is proposed to show how a residual can be generated in this manner. Simulation results on the fault detection of a mass‐spring‐damper system show the effectiveness of the proposed method.
Estimation of the production index of oil and gas from the reservoir into the well during under-balanced drilling (UBD) is studied. This paper compares a Lyapunov-based adaptive observer and a joint unscented Kalman filter (UKF) based on a low order lumped (LOL) model and the joint UKF based on the distributed drift-flux model by using real-time measurements of the choke and the bottom-hole pressures. Using the OLGA simulator, it is found that all adaptive observers are capable of identifying the production constants of gas and liquid from the reservoir into the well, with some differences in performance. The results show that the LOL model is sufficient for the purpose of reservoir characterization during UBD operations. Robustness of the adaptive observers is investigated in case of uncertainties and errors in the reservoir and well parameters of the models.
According to the repeated-game theory, the continual interactions of electricity producers can increase the probability of collusion between these firms. As a result, the market equilibrium will be established based on tacit collusion. In this paper, the model of collusion in a power pool market is presented in the form of a mathematical program with equilibrium constraint (MPEC). This program takes into account the transmission network and production capacity constraints of the power plant, and the uncertainty property caused by the demand shock. The uncertainty is shown by a random variable in this scenario. Assuming that the probability density function of the random variable is general knowledge for all stakeholders of the electricity market, and the risk tolerance is zero, the collusion resulting from the strategy due to uncertainty is shown as the market equilibrium. Finally, it is concluded that the collusive expected profits in anticipation of a high demand market are higher than a low demand market.
This paper is concerned with robust identification of processes with time-varying time delays. In reality, the delay values do not simply change randomly, but there is a correlation between consecutive delays. In this paper, the correlation of time delay is modeled by the transition probability of a Markov chain. Furthermore, the measured data are often contaminated by outliers, and therefore, t-distribution is adopted to model the measurement noise. The variational Bayesian (VB) approach is applied to estimate the model parameters along with time delays. Compared with the classical expectation-maximization algorithm, VB approach has the advantage of capturing the uncertainty of the estimated parameter and time delays by providing their full probabilities. The effectiveness of the proposed method is demonstrated by both a numerical example and a pilot-scale hybrid-tank experiment.
In this paper, we consider an important practical industrial process identification problem where the time delay can change at every sampling instant. We model the time-varying discrete time-delay mechanism by a Markov chain model and estimate the Markov chain parameters along with the time-delay sequence simultaneously. Besides time-varying delay, processes with both time-invariant and time-variant model parameters are also considered. The former is solved by an expectation-maximization (EM) algorithm, while the latter is solved by a recursive version of the EM algorithm. The advantages of the proposed identification methods are demonstrated by numerical simulation examples and an evaluation on pilot-scale experiments.
Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system's performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.
In this paper, a recurrent neural network coupled with Kalman filter is proposed to identify dynamic terms of robotic manipulator. By cooperating some inherent characteristics of robot, this network has the capability to individually identify nonlinear terms using Weighted Augmentation Error (WAE). To present the infrastructure of architecture, an adaptive scheme based on the conventional Back Propagation (BP) is firstly driven using the Gradient Descent (GD) method. Additionally, a stable adaptive updating rule is extracted from the discrete time Lyapunov candidate as an approach for the general nonlinear system identification. Then, this approach is applied to the predefined network. To experimentally validate the computational efficiency and control applicability of the proposed method, Adaptive Neural Network Based Inverse Dynamic Control (ANN-Based-IDC) is employed on a laboratory-scaled twin-rotor CE-150 helicopter. This experiment illustrates enhancement of steady-state performance from 2-to-3 times more in compared with simple PID. Moreover, disturbance rejection and robustness tests admit capability of the method for online dynamic identification in the presence of output and dynamic perturbation.
In dealing with model predictive controllers (MPC), controller tuning is a key design step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants can be approximated by first order plus dead time models. The performance of such methods deteriorates in dealing with unknown or time-varying parameter plants. To overcome this problem, adaptive MPC tuning strategies are practical alternatives. The adaptive MPC tuning approach proposed in this paper is based on on-line identification and analytical tuning formulas. Simulation results are used to show the effectiveness of the proposed methodology. Also a comparison of the proposed adaptive tuning method with a well-known online tuning method is presented briefly which shows superiority of the proposed adaptive tuning method.
In the recent years, artificial neural network have been used to improvement of system identification. The performance of neural network directly depends on the hidden layer, which include weights and activation functions of the network. In addition Genetic Algorithms are used to learn of neural network as a type of evolutionary computing algorithms. In this paper, the structure of hidden layers and weights are modified by using biological neuron model of Izhikevich. These two methods, Genetic Algorithms and biological model of neuron, merge together for designing a novel structure.
In this letter, we point out that the asymptotic convergence, claimed in Theorem 2, of the output residual and parameter estimation error after fault occurrence are guaranteed by the performance of the fault diagnosis observer is not quite right. The proof of the asymptotic convergence is contributed by Lemma 1 and negative semidefiniteness of the first difference of Lyapunov candidate function. Here, it is shown that utilizing Lemma 1 yields in some disputed points in the proof of Theorem 2. On the other hand, the proof of Theorem 2 is not mathematically correct. Therefore, the guarantee of the asymptotic convergence mentioned for FD observer in Theorem 2 is not realizable.
This paper investigates the problem of decentralized model reference adaptive control (MRAC) for a class of large scale systems with time varying delays in interconnected terms and state and input delays. The upper bounds of the interconnection terms are considered to be unknown. Time varying delays in the nonlinear interconnection terms are bounded and nonnegative continuous functions and their derivatives are not necessarily less than one. Moreover, a simple and practical method based on periodic characteristics of reference model is established to predict the future states and input delay compensation. It is shown that the solutions of uncertain large-scale time-delay interconnected system converge uniformly exponentially to a desired small ball. The effectiveness of the proposed approaches are illustrated by a numerical example and a chemical reactor system.
This paper investigates the problem of decentralized model reference adaptive control (MRAC) for a class of large-scale systems with time-varying delays in the interconnected terms and state and input delays. The upper bounds of interconnection terms with time- varying delays and external disturbances are assumed to be completely unknown. By integrators inclusion, a dynamic input delay compensator is established for input delay compensation and it is used as a practical method for state calculation x (t+ R). Also, a method is presented for a class of decentralized feedback controllers, which can evolve the closed-loop system error uniformly bounded stable. As a numerical example, the proposed technique is applied to an unstable open-loop system to show the feasibility and effectiveness of the method.
A direct adaptive tuning strategy is proposed for model predictive controllers. Parameter tuning is essential for a satisfactory control performance. Various tuning methods are proposed in the literature which can be categorised as heuristic, numerical and analytical methods. The proposed tuning methodology is based on an analytical model predictive control tuning approach for plants described by first-order plus dead time models. For a fixed tuning scheme, the tuning performance deteriorates in dealing with unknown or time varying plants. To overcome this problem, an adaptive tuning strategy is utilised. It is suggested to employ a discrete-time model reference adaptive control with recursive least squares estimations for controller tuning. The proposed method is also extended to multivariable systems. The stability and convergence of the proposed strategy is proved using the Lyapunov approach. Finally, simulation and experimental studies are used to show the effectiveness of the proposed methodology.
University ranking systems attempt to provide an ordinal gauge to make an expert evaluation of the university’s performance for a general audience. University rankings have always had their pros and cons in the higher education community. Some seriously question the usefulness, accuracy, and lack of consensus in ranking systems and therefore multidimensional ranking systems have been proposed to overcome some shortcomings of the earlier systems. Although the present ranking results may rather be rough, they are the only available sources that illustrate the complex university performance in a tangible format. Their relative accuracy has turned the ranking systems into an essential feature of the academic lifecycle within the foreseeable future. The main concern however, is that the present ranking systems totally neglect the ethical issues involved in university performances. Ethics should be a new dimension added into the university ranking systems, as it is an undisputable right of the public and all the parties involved in higher education to have an ethical evaluation of the university’s achievements. In this paper, to initiate ethical assessment and rankings, the main factors involved in the university performances are reviewed from an ethical perspective. Finally, a basic benchmarking model for university ethical performance is presented.
This note deals with the problem of controlling an uncertain multivariable plant in the presence of input saturation via switching among a finite family of controllers having a generalized anti-windup architecture. The problem is addressed within the multi-model unfalsified adaptive switching control framework. It is shown that proper definitions of fictitious references and test functionals allow to prove stability of the overall switching scheme, provided that at least one controller in the finite family is stabilizing. The satisfiability of this assumption is discussed and simulation results are reported.
State estimation for a system with irregular rate and delayed measurements is studied using fusion Kalman filter. Lab data in process plants is usually more accurate compared to other measurements. However, it is often slow rate and subject to variable delay and irregularity in sampling time. Fast rate state estimation can be conducted using fast rate measurement, while the slow rate lab data can be used to improve the accuracy of estimation whenever it is available. For this purpose, two Kalman filters are used to estimate the states based on each type of measurement. The estimates are fused in the next step by considering the correlation between them. An iterative algorithm to obtain the cross-covariance matrix between the estimation errors of the two Kalman filters is presented and employed in the fusion process. The improvement on the accuracy of estimation and comparison with other optimal fusion state estimation techniques are discussed through a simulation example, a pilot-scale experiment and an industrial case study.
In this paper, Linear Quadratic Gaussian (LQG) controller extended to a class of nonlinear systems based on subspace matrices using bilinear model. LQG controller design based on subspace matrices provides directly from system input output data. Therefore it is more useful for systems that their models are not available. Since the most practical systems are nonlinear, LQG controller design based on linear subspace model is reflected to a weak control performance or even instability. To overcome this problem, LQG controller design based on bilinear subspace model is presented. Simulation results and comparison studies are provided to show the effectiveness of proposed method.
This paper proposes a model bank selection method for a large class of nonlinear systems with wide operating ranges. In particular, nonlinearity measure and H-gap metric are used to provide an effective algorithm to design a model bank for the system. Then, the proposed model bank is accompanied with model predictive controllers to design a high performance advanced process controller. The advantage of this method is the reduction of excessive switch between models and also decrement of the computational complexity in the controller bank that can lead to performance improvement of the control system. The effectiveness of the method is verified by simulations as well as experimental studies on a pH neutralization laboratory apparatus which confirms the efficiency of the proposed algorithm.
This paper considers the H 2 filtering problem for continuous-time descriptor systems by revisiting the H 2 performance and introducing the new formulation. Differing from previous results, recent note provides solvability conditions of the H 2 filtering problem with both the singular and the normal filters. The results are introduced as necessary and sufficient conditions for the singular filters and as sufficient conditions for the normal filters. These conditions are extracted without decomposing the original system matrices and are expressed in terms of strict linear matrix inequalities (LMIs). A numerical example with simulation results is given to illustrate the effectiveness of the proposed methods.
Measuring the contour boundary irregularities of skin lesion is an important factor in early detection of malignant melanoma. On the other hand, cancer is usually recognized as a chaotic growth of cells. It is generally assumed that boundary irregularity associated with biomedical images may be due to the chaotic behavior of its originated system. Thus, chaotic indices can serve as some criteria for classifying dermoscopy images. In this paper, a new approach is presented for extraction of Lyapunov exponent and Kolmogorov–Sinai entropy in the skin lesion images. This method is based on chaotic time series analysis. Converting the region of interest of skin lesion to a time series, reconstruction of system phase space, estimation of the Lyapunov exponents and calculation of Kolmogorov–Sinai entropy are the steps of the proposed approach. The combination of the largest Lyapunov exponent and Kolmogorov–Sinai entropy is selected as a criterion for distinction between melanoma and mole categories. Experiments on a set of dermoscopy images yielded a sensitivity of 100% and a specificity of 92.5% providing superior diagnosis accuracy compared to other related similar works.
In This study, we present a new sensor fault detection approach based on nonlinear parity technique in presence of sensor noise. Conventionally analytical redundancy (AR) was used to fault detection and isolation in linear systems. The proposed parity space approach with nonlinear analytical redundancy (NLAR) technique can be applied to detect sensor fault in the nonlinear affine systems with mentioned class. The proposed approach will be implemented in pH neutralization system. At the end nonlinear fault detection and identification algorithm will be successfully implemented, examined and reported.
A spatially-constrained clustering algorithm is presented in this paper. This algorithm is a distributed clustering approach to fine-tune the optimal distances between agents of the system to strengthen the data passing among them using a set of spatial constraints. In fact, this method will increase interconnectivity among agents and clusters, leading to improvement of the overall communicative functionality of the multi-robot system. This strategy will lead to the establishment of loosely-coupled connections among the clusters. These implicit interconnections will mobilize the clusters to receive and transmit information within the multi-agent system. In other words, this algorithm classifies each agent into the clusters with the lowest cost of local communication with its peers. This research demonstrates that the presented decentralized method will actually boost the communicative agility of the swarm by probabilistic proof of the acquired optimality. Hence, the common assumption regarding the full-knowledge of the agents’ primary locations has been fully relaxed compared to former methods. Consequently, the algorithm’s reliability and efficiency is confirmed. Furthermore, the method’s efficacy in passing information will improve the functionality of higher-level swarm operations, such as task assignment and swarm flocking. Analytical investigations and simulated accomplishments, corresponding to highly-populated swarms, prove the claimed efficiency and coherence.
The topic of control performance assessment techniques has drawn lots of attentions and many performance assessment indices have been proposed. These indices are focused on certain malfunctions. Fallacious decision would result in if it is based on individual indices. Therefore, fusion of different indices can improve the assessment accuracy. In this paper, Bayesian and Dempster-Shafer theory are used individually to establish decision fusion strategy and tackle the performance assessment challenge of heavy duty gas turbine. To study the uncertainty effect on these methods, they are applied to the well-known Rowen model of gas turbine. The results illustrate the effectiveness of the proposed performance assessment method of the gas turbine model on the one hand, and the superiority of the Bayesian method when the uncertainty is low and that of Dempster-Shafer theory in the presence of uncertainty, on the other hand.
In this study, a novel robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model. It considers a larger category of nonlinear system when fuzzification is used for only nonlinear distribution matrices. In fact the proposed method covers nonlinear systems could not transform to linear T-S model. This paper studies the problem of robust fault diagnosis based on two fuzzy nonlinear observers, the first one is a fuzzy nonlinear unknown input observer (FNUIO) and the other is a fuzzy nonlinear Luenberger observer (FNLO). This approach decouples the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design FNUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method; meanwhile, FNLO is designed for faulty subsystem to generate fuzzy residual signal based on a quadratic Lyapunov function and some matrices inequality convexification techniques. FNUIO affects only the fault free subsystem and completely removes any unknown inputs such as disturbances when residual signal is generated by FNLO is affected by component or sensor fault. This novelty and using nonlinear system in T-S model make the proposed method extremely effective from last decade literature. Sufficient conditions are established in order to guarantee the convergence of the state estimation error. Thus, a residual generator is determined on the basis of LMI conditions such that the estimation error is completely sensitive to fault vector and insensitive to the unknown inputs. Finally, an numerical example is given to show the highly effectiveness of the proposed fault diagnosis scheme.
In this study, a novel fuzzy unknown input observer for robust fault estimation scheme is developed when both faults and unknown input are considered. The proposed scheme includes component fault with nonlinear distribution matrix in state equation, unknown input signal in state and output equations. After that, Takagi-Sugeno (T-S) model is used to create multiple models. While T-S model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear system will be included. Two set of observers are considered, the first one is extended fuzzy unknown input observer (EFUIO) and the other one is fuzzy sliding mode observer (FSMO). The approach decoupled the faulty subsystem from the rest of the system through a series of linear transformations. Then, the objective is to design EFUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method. Unknown input is removed; meanwhile, FSMO is designed for faulty subsystem to guarantee estimation of fault. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities (LMIs). Finally, a simulation study on an electromagnetic suspension system (EMS) is presented to demonstrate the performance of the results compared with a pure SMO.
This paper considers the robust H ∞ filtering problem for uncertain discrete-time descriptor systems. A class of uncertain systems with norm-bounded uncertainties is considered. The necessary and sufficient condition for solvability of the robust full-order H ∞ filtering is introduced which is generally less conservative than those existing sufficient conditions only. Explicit expressions of these filters are given. In addition to the full-order filtering problem, the robust reduced-order H ∞ filtering is also addressed by using slack variables technique in new sufficient conditions. The parameters of reduced-order filters are directly extracted from the solvability conditions. All the above conditions are convex and are expressed in term of linear matrix inequalities (LMIs) by using the original system matrices. The results generalize the previously developed H ∞ filter design for standard discretetime systems. A numerical example is presented to demonstrate the effectiveness of the proposed approaches.
This paper addresses a multimodel unfalsified adaptive switching control with finite fixed time window cost function by utilizing a self-falsification strategy. A closed-loop stability proof is provided, and it is shown that the forgetting factor employed with finite fixed windowed cost function improves the closed-loop performance. Furthermore, it is shown that the unfalsified adaptive control with nonmonotone cost function is unable to select the appropriate controller, and a new reset strategy is proposed to resolve this problem. The γ sequence monotonicity in the linear increasing cost-level algorithm causes a performance deterioration, and a γ sequence reset is introduced for performance enhancement. Effectiveness of the proposed method is investigated for a nonlinear pH neutralization process and the 2-cart benchmark example.
We consider a drift-flux model (DFM) describing multiphase (gas-liquid) flow during drilling. The DFM uses a specific slip law, which allows for transition between single and two phase flows. With this model, we design unscented Kalman filter (UKF) and extended Kalman filter (EKF) for the estimation of unmeasured state, production, and slip parameters using real-time measurements of the bottom-hole pressure, outlet pressure, and outlet flow rate. The OLGA high-fidelity simulator is used to create two scenarios from underbalanced drilling on which the estimators are tested: a pipe connection scenario and a scenario with a changing production index (PI). A performance comparison reveals that both UKF and EKF are capable of identifying the PIs of gas and oil from the reservoir into the well with acceptable accuracy, while the UKF is more accurate than the EKF. Robustness of the UKF and EKF for the pipe connection scenario is studied in case of uncertainties and errors in the reservoir and well parameters of the model. It is found that these methods are very sensitive to errors in the reservoir pore pressure value. However, they are robust in the presence of error in the liquid density value of the model.
State estimation and fusion is studied using Kalman filter (KF) when a slow-rate integrated measurement is available. Integrated measurement is common in industrial processes, when a sample of material is gradually collected over some period of time and then sent to a laboratory for analysis. In this case, the laboratory measurement will reflect the material properties that have been integrated over the sampling period. The goal is to estimate the fast-rate value of states that evolve with time. A modified KF is proposed to execute state estimation using a slow-rate integrated measurement. Fusion of the slow-rate state estimate and other fast-rate measurements can improve the final state estimation of the process. The performance of the proposed method is demonstrated through both simulation and experimental study in a laboratory scale hybrid tank pilot plant.
In this paper, model based fault detection of gas turbine using linear and non-linear methods (multilayer perceptron and radial basis function neural network models) is studied. We contemplate IGV positions and gas flow as input and sensors related to compressor as outputs. Then residual signals will be obtained based on system model. In addition, by these signals and exert the fixed and adaptive thresholds, the fault occurred in the V94. 2 gas turbine which is pollution of vane compressor (Fouling detection) has identified and diagnosed. Consequently, by comparing the obtained results from different fault detection methods, we determine the most appropriate signal output that led to better and reliable result. All simulations have been carried out by using real data taken from an V94. 2 industrial gas turbine 927 power plant in Fars.
In dealing with model predictive controllers (MPC), controller tuning is a key design step. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a recently proposed analytical MPC tuning approach based on low order models. The performance of such methods deteriorates in dealing with unknown or time varying parameter plants. To overcome this problem, adaptive MPC tuning strategies are practical alternatives. The adaptive MPC tuning approach proposed in this paper is based on on-line identification and analytical tuning formulas. Simulation results are used to show the effectiveness of the proposed methodology.
Filtering is an effective method of alarm management family that can reduce false and missed alarm rates significantly. Simple and effective techniques of fault diagnosis methods are popular in industry. So, deriving a simple analytic filter design approach is important. This study proposes a simple analytic linear filter design based on a probabilistic model of the system. At last, the effectiveness of the proposed method is showed in the deposition fault detection of a V94. 2 gas turbine with 162.1 MW and 50 Hz as the nominal power and frequency respectively. It is built by MAPNA group (originally built by SIEMENS) and set up in Shiraz power plant, Shiraz city of Iran.
The main goal of this paper is modelling attention while using it in efficient path planning of mobile robots. The key challenge in concurrently aiming these two goals is how to make an optimal, or near-optimal, decision in spite of time and processing power limitations, which inherently exist in a typical multi-sensor real-world robotic application. To efficiently recognise the environment under these two limitations, attention of an intelligent agent is controlled by employing the reinforcement learning framework. We propose an estimation method using estimated mixture-of-experts task and attention learning in perceptual space. An agent learns how to employ its sensory resources, and when to stop observing, by estimating its perceptual space. In this paper, static estimation of the state space in a learning task problem, which is examined in the WebotsTM simulator, is performed. Simulation results show that a robot learns how to achieve an optimal policy with a controlled cost by estimating the state space instead of continually updating sensory information.
Reliable controllers with high flexibility and performance are necessary for the control of intricate, advanced, and expensive systems such as aircraft, marine vessels, automotive vehicles, and satellites. Meanwhile, control allocation has an important role in the control system design strategies of such complex plants. Although there are many proposed control allocation methodologies, few papers deal with the problems of infeasible solutions or system matrix singularity. In this paper, a pseudo inverse based method is employed and modified by the null space, least squares, and singular value decomposition concepts to handle such situations. The proposed method could successfully give an appropriate solution in both the feasible and infeasible sections in the presence of singularity. The analytical approach guarantees the solution with pre-defined computational burden which is a noticeable privilege than the linear and quadratic optimization methods. Furthermore, the algorithm complexity is proportionately grown with the feasible, infeasible, and singularity conditions. Simulation results are used to show the effectiveness of the proposed methodology.
Control performance assessment techniques are widely studied and many performance assessment indices have been proposed. In this paper, a control performance assessment technique for multi-loop control systems is presented based on the decision fusion strategy. Since decisions based on individual indices can lead to erroneous results, decision fusion of different indices can improve the assessment accuracy, especially in multi- loop control systems in the presence of loop interactions. Performance assessment indices are individually evaluated and decisions based on these indices are fused. The results of simulation and practical implementation on series cascade control structures illustrate the effectiveness of the proposed algorithm.
Fault‐tolerant control systems are vital in many industrial systems. Actuator redundancy is employed in advanced control strategies to increase system maneuverability, flexibility, safety, and fault tolerability. Management of control signals among redundant actuators is the task of control allocation algorithms. Simplicity, accuracy and low computational cost are key issues in control allocation implementations. In this paper, an adaptive control allocation method based on the pseudo inverse along the null space of the control matrix (PAN) is introduced in order to adaptively tolerate actuator faults. The proposed method solves the control allocation problem with an exact solution and optimized l∞ norm of the control signal. This method also handles input limitations and is computationally efficient. Simulation results are used to show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.
In this study, a novel fuzzy robust fault estimation scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes component fault with a nonlinear distribution matrix; as a result, the Takagi–Sugeno model is used to create multiple models. While the Takagi–Sugeno model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear systems will be considered. This paper presents the problem of robust fault estimation based on fuzzy nonlinear observers, the first one is a fuzzy unknown input observer and the other one is a fuzzy sliding mode observer. The approach decoupled the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design a fuzzy unknown input observer guaranteeing the asymptotic stability of the error dynamic using the Lyapunov method and completely removing disturbances; meanwhile, a fuzzy sliding mode observer is designed for a faulty subsystem to generate an estimation of fault based on a quadratic Lyapunov function and some matrices inequality convexification techniques. The sliding motion affects only the faulty subsystem through a novel reduced order fuzzy sliding mode observer; meanwhile, all disturbances are completely removed by fuzzy unknown input observer. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities. Thus, an exact fault estimator is determined on the basis of linear matrix inequality conditions while the estimation fault is completely insensitive to the disturbance. Finally, a simulation study on an electromagnetic suspension system is presented to demonstrate the g performance of the results compared with a pure sliding mode observer.
This paper presents a Gaussian radial basis function neural network based on sliding mode control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. The adaptation laws of designed controller are obtained based on sliding mode control methodology without calculating the Jacobian of the flexible joint system. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSERâs flexible-joint manipulator.
This paper addresses the problem of velocity estimation for a class of uncertain mechanical systems. Using advantages of immersion and invariance technique with input– output filtered transformation, a proper immersion and dynamical auxiliary filter have been constructed in the designed estimator. Uniform global asymptotic convergence of the velocity estimator has been proved for the system with parametric uncertainties. In the presence of perturbations on the input and output, the performance analysis of the estimator has been theoretically investigated and illustrated by simulation results.
This study presents a novel indirect adaptive hierarchical fuzzy sliding mode controller for a class of high-order SISO nonlinear systems in normal form with unknown functions in the presence of bounded disturbance. The hierarchical fuzzy system is able to reduce the number of rules and parameters with respect to ordinary fuzzy systems. On-line tuning algorithm for consequent part parameters of fuzzy rules in different layer of hierarchical fuzzy system is derived using defined Lyapunov function. Two theorems are proved to show that the suggested adaptive schemes can achieve asymptotically stable tracking of a reference input with guarantee of the bounded system signals. One for unity control gain and the other for non-unity control gain. To show the effectiveness of the proposed method, control of three systems are considered in the simulations. The simulations results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic system.
Control of hybrid systems faces computational complexity as a main challenging problem. To reduce the computational burden, multi-parametric programming has been proposed to obtain the explicit solution of the optimal control problems for some classes of hybrid systems. This strategy provides the solution as a function of the state variables which can be obtained in an off-line fashion. A shortcoming of this technique is that the complexity of the explicit solution is again prohibitive for large problems. The main contribution of this paper is the introduction of an approximation algorithm for solving a general class of multi-parametric mixed-integer linear programming (mp-MILP) problems. The algorithm selects those binary sequences that make significant improvement in the objective function, if considered. It is shown that significant reduction in computational complexity can be achieved by introducing adjustable level of suboptimality. A family of suboptimal controllers is obtained by the proposed approach for which the level of error and complexity can be adjusted by a tuning parameter. It is shown that no part of the parameter space is disregarded during the approximation. Also it is proved that the error in the achieved approximate solutions is a monotonically increasing function of the tuning parameter. Assuming that the closed-loop stability is ensured by including some constraints in the formulation of hybrid control, it will be preserved by the suboptimal low-complexity controllers. Illustrative examples are presented to demonstrate the achieved complexity reduction.
In this contribution, full probability distribution of parameters of ARX model is obtained for on-line problems by means of Bayesian approach and Markov chain Monte Carlo method (MCMC), which provides the ability to be applied on time-varying ARX models as well. Full probability distribution of parameters represent whole available knowledge of parameters. So, decision makers can follow any policies to make decision about point estimation, like dynamic point estimation. Moreover, the Bayesian approach has great potential in combining sources of knowledge much more easier. To decrease the computational efforts, full probability of model parameters are updated based on size-varying partitions. Furthermore, incorporating the posterior probability of previous partition into the jump probability of current partition, in MCMC method, improves the performance of the proposed algorithm from the computation and convergence rate point of view. Simulation results demonstrate the effectiveness and validity of the proposed algorithm.
Optimal solution for nonlinear identification problem in the presence of non-Gaussian distribution measurement and process noises is generally not analytically tractable. Particle filters, known as sequential Monte Carlo method (SMC), is a suboptimal solution of recursive Bayesian approach which can provide robust unbiased estimation of nonlinear non-Gaussian problem with desire precision. On the other hand, Hunt-Crossley is a widespread nonlinear model for modeling telesurgeries environment. Hence, in this paper, particle filter is proposed to capture most of the nonlinearities in telesergerie environment model. An online Bayesian framework with conventional Monte Carlo method is employed to filter and predict position and force signals of environment at slave side respectively to achieve transparent and stable bilateral teleoperation simultaneously. Simulation results illustrate effectiveness of the algorithm by comparing the estimation and tracking errors of sampling importance resampling (SIR) with extended Kalman filter.
This paper presents a simple analytical method for tuning the parameters of fractional order PI (FOPI) controllers based on Bode's ideal transfer function. The proposed technique is applicable to stable plants describable by a fractional order counterpart of first order transfer function without time delay. Tuning rules are given in order to improve the robustness of the compensated system in the presence of gain uncertainty in the plant model. Finally, the designed FOPI controller is implemented on a laboratory scale twin rotor helicopter and comparison results are provided to show the effectiveness of the proposed tuning rules.
Gaussian process (GP) regression is a fully probabilistic method for performing non-linear regression. In a Bayesian framework, regression models can be made robust by using heavy-tailed distributions instead of using normal distribution for modeling noise. This work focuses on estimation of parameters for robust GP regression. In literature, these are learned by maximizing the approximate marginal likelihood of data. However, gradient-based optimization algorithms which are used for this purpose can be unstable or may require tuning. In this work, an EM algorithm based approach is derived and implemented to infer the parameters. The pros and cons of the two approaches are analyzed. The advantage of EM algorithm lies in its ease of implementation and theoretical guarantees of numerical stability and convergence while its prediction performance is still comparable to gradient-based approaches. In some cases EM algorithm may be slow to converge. To circumvent this issue a faster EM based approach known as Expectation Conjugate Gradient (ECG) is implemented on robust GP regression. Finally, the proposed EM approach to robust GP regression is validated using an industrial data set.
In this paper, we present a new method for obtaining closed-form SDC matrices for synthesis of SDRE controller for non-affine nonlinear systems. Furthermore, we design SDRE controller with OCU method for proposed SDC form. Simulation results shows that proposed method for designing SDRE controller yields good tracking performance and smoothness of control signals. Robustness of the designed SDRE controller will be illustrated to a class of external disturbances.
In this paper, particle dynamics and stability analysis of gravitational search algorithm (GSA) are investigated. The GSA is a swarm optimization algorithm which is inspired by the Newtonian laws of gravity and motion. Previously, the convergence analysis of the GSA and improved GSA algorithms were presented to demonstrate each particle converges. In this study, the stability of the particle dynamics using Lyapunov stability theorem and the system dynamics concept is analyzed. Sufficient conditions of stability analysis are investigated and utilized for adapting parameters of the GSA. The modified algorithm based on stability analysis is compared with the standard GSA, PSO, RGA, and two methods of improved GSA in terms of average, median, and standard deviation of best-so-far solutions. Simulation results demonstrate the validity and feasibility of the proposed modified GSA. In solving the optimization problem of various nonlinear functions, the high performance is achieved.
In this paper, a novel scheme is presented to conquer the motion-planning problem for autonomous space robots. Minimizing the consumed energy of atomic batteries within the daily planetary missions of robot on the planet is taken into account, i.e., utilization of the generated solar power by its embedded photocells leads to saving energy of batteries for night missions. Aforementioned objective could be acquired by appropriate interaction of motion planning paradigm with shadows of obstacles. Modeling of the shadow with the proposed artificial potential field leads to generalize the concept of potential fields not only for static and dynamic obstacles but also for being confronted with the intrinsic time-variant phenomena such as shadows. With due attention to the noticeable computational complexity of the introduced strategy, fuzzy techniques are applied to optimize the sampling times effectively. To accomplish this objective, a smart control scheme based on the fuzzy logic is mounted to the primitive version of algorithm. Regarding the need to identify some structural parameters of obstacles, PIONEER™ mobile robot is designed as a test bed for the verification of simulated results. Investigation on empirical accomplishments shows that the goal-oriented definition of Time–Variant Artificial Potential Fields is able to resolve the motion-planning problem in planetary applications.
In this paper, a novel architecture in multilayer perceptron (MLP) neural network with flexible activation function and adaptive learning rate is presented for a data-driven identification of robot dynamics. It is assumed that the measurement of robot end-effector position, velocity and acceleration are available corrupted by Gaussian noise. Since some general property of robot dynamics are included in the proposed structure as well as optimization indices, this structure is envisaged having good performance in confronting with uncertainty in measurements. The main contribution of this paper is to propose a transparent neural network structure for identification of dynamic terms by introducing a gray-box identifier. Simulation results on 2-DOF serial manipulator reveal the accuracy of the method. Finally, experimental results on a laboratory-scaled twin rotor CE 150 helicopter indicate the applicability of the proposed method.
This paper presents a modified Independent Component Analysis (ICA)-based Fault Detection Method (FDM). The proposed FDM constructs a set of matrices, revealing the trend of the variable samples and execute ICA algorithm for each set of matrices in contrast to the FDM based on dynamic ICA (DICA) which constructs the high imensional augmented matrix. This paper shows that the proposed FDM decreases the matrix dimensions and as result compensates for some disadvantages of using the high dimensional matrix discussed in previous articles. Furthermore, other advantages of the proposed FDM are the decreases in the running time, computational cost of the algorithm and the orthogonalization estimation errors. Moreover, the proposed method improves the detectability for a class of faults compared to DICA-based FDM. This class of fault occurs when two or more consecutive samples of fault source signal have opposite signs and cancel out each other. Simulation results are provided to show the effectiveness of the proposed methodology.
In this paper, an energy-based control methodology is proposed to satisfy the Reynolds three rules in a flock of multiple agents. First, a control law is provided that is directly derived from the passivity theorem. In the next step, the Number of Neighbours Alignment/Repulsion algorithm is introduced for a flock of agents which loses the cohesion ability and uniformly joint connectivity condition. With this method, each agent tries to follow the agents which escape its neighbourhood by considering the velocity of escape time and number of neighbours. It is mathematically proved that the motion of multiple agents converges to a rigid and uncrowded flock if the group is jointly connected just for an instant. Moreover, the conditions for collision avoidance are guaranteed during the entire process. Finally, simulation results are presented to show the effectiveness of the proposed methodology.
Model predictive controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly calculated. The proposed method is validated via simulations for both slow and fast time varying systems. Simulation results indicate the controller ability for tracking references in the presence of plant’s time varying parameters. In addition, an analytical tuning method for adjusting prediction horizon is proposed based on optimization of the objective function. It leads to a simple formula including the model parameters, and an indirect adaptive controller can be designed based on the analytical formula. Simulation results indicate a better performance for the tuned controller. Finally, experimental tests are performed to show the effectiveness of the proposed methodologies.
In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems.
In this paper a novel method for adaptive predictive control of a launch vehicle is presented. Nonlinear dynamics of these systems cause challenging problems in controller design. Linearizing the system in diverse operating points and designing appropriate controllers for these systems is an interesting idea in industry. The outcome is a linear time varying (LTV) system. Dealing with time varying dynamics is a challenging issue in control theory. Adaptive control approach presents a well-established methodology to address the subject of flight control systems. This paper proposes an indirect adaptive predictive idea to control the pitch channel dynamics of a launch vehicle. For this purpose, a robust estimator and a robustly-tuned generalized predictive controller are incorporated to present a robust adaptive scheme. The proposed technique is applied to pitch channel model of Vanguard missile. A set of test scenarios is conducted to explore the performance of proposed controller in various conditions. The results demonstrate the fidelity of this method to yield high performance in the presence of time-varying parameters under various un-modeled dynamics and external disturbances.
In this study, a new strategy for fault detection and isolation is presented. This strategy is based on the design of a Lüneburg observer which is implemented via pole placement using linear matrix inequalities. Two residuals are formulated based on the state estimation error in order to be utilized in detecting and isolating faults happened on the system. Fault detection problem solves by changes occur in the residual value and fault isolation is done through determining threshold on residuals according to system behavior in faulty condition. The procedure performs in four simulations steps in which there are certain numbers of faults happen in the system in each step. This method is validated in simulation on a quadruple tank process while each faulty condition is considered as a leak at the bottom of a tank in the process. This can lead to an undesirable flow of liquid out of the tank which results to a decrease in tank's level. The simulation results represented in the paper shows the applicability of this strategy.
In this paper, an analytical method for tuning the parameters of the set-point weighted fractional order PID (SWFOPID) controller is proposed. The studied control scheme is the filtered fractional set-point weighted (FFSW) structure. Also to achieve a desired closed-loop performance, a fractional order pre-filter is employed. The proposed method is applicable to stable plants describable by a simple three-parameter fractional order model. Such a model can be considered as the fractional order counterpart of a first order transfer function without time delay. Finally, the proposed method is implemented on a laboratory scale CE 150 helicopter platform and the results are compared with those of applying a filtered fractional order PI (FFOPI) controller in a similar structure. The practical results show the effectiveness of the proposed method.
Unmanned Aerial Vehicles (UAVs) pose a multi-input and multi-output (MIMO) dynamic structure, making their simultaneous guidance and control too complicated to be maintained via conventional scalar controllers. In this paper, a multivariable optimal controller is introduced based upon LQG\LTR design approach to effectively control the UAV attitude in the presence of noise and disturbance. The regulator design problem is solved by generating an optimal state estimate using a Kalman filter. A loop transfer recovery (LTR) procedure is developed to allow good recovery of the full state feedback properties, enhancing stability and performance robustness. This scheme facilitates proper integration of system's gain at different frequencies in order to provide optimal bandwidth and yet weakening the noise effects. The corresponding rate of return gains is set in frequency-domain to achieve robust performance characteristics. A set of tests is conducted on an UAV simulation case study to explore its performance under different scenarios. The results clearly demonstrate well performances in the face of the induced noise and couplings between the system channels.
Many industrial processes can be effectively described with first-order plus fractional dead time models. In the case of plants with a large dead time relative to the time constant, approximations in discretizing the time delay can adversely affect the performance and if the sample time is enforced by system requirements, the fractional nature of the delay should be considered. In this paper, an analytical approach to model predictive control tuning for stable and unstable first-order plus dead time models with fractional delay is presented. The existing tuning methods are based on trial and error or numerical optimization approaches and the available closed form equations are limited to plants with integer delays. In this paper, an analytical approach is adopted and the issues of closed loop stability and achievable performance are addressed. Finally, simulation results are used to show the effectiveness of the proposed tuning strategy.
This paper considers the problem of controlling coupled chaotic maps. Coupled chaotic maps or multichaotic subsystems are complex dynamical systems that consist of several chaotic sub-systems with interactions. The OGY methodology is extended to deal with the control of such systems. It is shown that the decentralized control design scheme in which the individual controllers share no information is not generally able to control multichaotic systems. Simulation results are used to support the main conclusions of the paper.
In this paper, control performance assessment for a class of nonlinear systems modelled by autoregressive second-order Volterra series with a general linear additive disturbance is presented. The proposed approach employs the nonlinear generalised minimum variance (NGMV) controller concept. The Volterra series models provide a natural extension of a linear convolution model with the nonlinearity considered in an additive term. The polynomial operator form is used throughout this paper for the description of the system input–output model. The closed form formulation of NGMV controller for autoregressive second-order Volterra series is presented in a polynomial form then a control assessment criterion based on the NGMV control is given. Simulation results and comparison studies are used to show the effectiveness of the proposed approach for a class of nonlinear systems.
In this paper, the problem of decentralized model reference adaptive control (MRAC) for a class of large scale systems with time varying delay in interconnected term and input and state delays is studied. To compensate the effect of input delay indirectly, a Smith predictor built on. To handle the effects of the time delays in input, the adaptive controller part includes two auxiliary dynamic filters with time varying gains. Under a usual assumption that the interconnections are assumed to be Lipschitz in its variables and uniformly in time with unknown Lipschitz gains, the difficulties from unknown interconnections are dealt. A generalized error is defined and by a suitable Lyapunov function, an adaptive controller is designed to stabilize it. Decentralized adaptive feedback controller can render the generalized error system uniformly ultimately bounded stable is designed. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed design techniques.
The main point of this paper is to present an iterative optimization strategy for tuning the parameters of Smith predictor based fractional order PID (SPFOPID) controller. The control scheme considered in this paper is the standard Smith predictor structure. Also, the internal model is considered to be a First Order Plus Dead Time (FOPDT) transfer function. Finally, the proposed method is implemented on a multi input-multi output (MIMO) flow-level plant and the obtained results are compared with the results of applying Smith predictor based PID controller (SPPID) in the similar structure.
The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2.
We present a simplified drift-flux model (DFM) describing a multiphase (gas-liquid) flow during drilling. The DFM uses a specific slip law, without flow-regime predictions, which allows for transition between single and two phase flows. With this model, we design an Unscented Kalman Filter (UKF) for estimation of unmeasured states, production parameters and slip parameters using real time measurements of the bottom-hole pressure and liquid and gas rate at the outlet. The performance is tested against the Extended Kalman Filter (EKF) by using OLGA simulations of typical drilling scenarios. The results show that both UKF and EKF are capable of identifying the production constants of gas from the reservoir into the well, while the UKF has better convergence rate compared with EKF.
A distributed drift-flux model and a low-order lumped model describing a multiphase (gas-liquid) flow in the well during Under-Balanced Drilling (UBD) has been presented. This paper presents a novel nonlinear adaptive observer to estimate the total mass of gas and liquid in the annulus and production constant of gas and liquid from the reservoir into the well during UBD operations. Furthermore, it describes a joint unscented Kalman filter to estimate parameters and states for both the distributed drift-flux and lumped model by using real-time measurements of the choke and the bottom-hole pressures. The performance of the adaptive observers are evaluated for typical drilling scenarios. The results show that all adaptive observers are capable of identifying the production index, although the adaptive observers based on the low-order lumped model achieves better convergence rate than adaptive observer based on the drift-flux model. The results show that the LOL model is sufficient for the purpose of estimating the production parameters.
This study presents fault detection of a heavy duty V94. 2 gas turbine which has 162.1 MW nominal power and 50 Hz nominal frequency and is located at Pareh Sar power plant, Gilan, Iran. For this purpose stored data include measurements of relative and absolute vibration of shaft bearings in both turbine and compressor sections. Signal processing techniques and mathematical transformations are used for feature extraction, as well as supervised and unsupervised methods for dimensionality reduction. Finally neural networks are employed for classification task and fault detection results for different methods are compared and discussed. Proposed techniques show zero FAR and MAR, when PNN is used with PCA or when MLP or RBF is used with LDA for dimensionality reduction.
to enhance the closed loop performance in presence of disturbance, uncertainties and delay a double loop mixture of MPC and robust controller is proposed. This double loop controller ensures smooth tracking for a 3-axis gyro-stabilized platform which has delay intrinsically. This control idea is suggested to eliminate high frequency disturbances and minimize steady state error with minimum power consumption in simulation and experiment. Proposed controller based on the combination of ℋ2 and ℋ∞ controllers in the inner control loop shows the robustness of the proposed methodology. In the outer loop to have a good tracking performance, an integrated MPC is used to handle delay in system dynamics. Also, the main idea for dealing with uncertainties is using integral and derivative of platform attitude. In the proposed platform, the ℋ∞ controller is compared with ℋ∞/ℋ2 controller in KNTU laboratory in theory and experiment. Results of experimental set up shows the same reaction of two controllers against disturbance and uncertainties in delayed system.
The problem discussed in this paper is the effect of latency time on the OGY chaos control methodology in multi chaotic systems. The Smith predictor, rhythmic and memory strategies are embedded in the OGY chaos control method to encounter loop latency. A comparison study is provided and the advantages of the Smith predictor approach are clearly evident from the closed loop responses. The complex plants considered are coupled chaotic maps controlled by the extended OGY scheme. Simulation results are used to show the effectiveness of the applied Smith predictor scheme structure in multi chaotic systems.
Unfalsified Adaptive Control (UAC) is a recently proposed robust adaptive control strategy. In this paper, the UAC principles and algorithms are reviewed and Multi-Model UAC is followed as an intermediate between UAC and multiple model control. Different approaches in UAC and MMUAC are studied. Also, Multi-Model Unfalsified Generalized Predictive control (MMUGPC) is proposed, which is a new control design strategy in the UAC framework. For an uncertain system, by utilizing several generalized predictive controllers and discrete switching between them with unfalsified control, a new structure is proposed and appropriate equations are derived. Simulation results show the effectiveness of proposed Multi-Model Unfalsified Generalized Predictive control.
This paper presents a non-linear generalised minimum variance (NGMV) controller for a second-order Volterra series model with a general linear additive disturbance. The Volterra series models provide a natural extension of a linear convolution model with the nonlinearity considered in an additive term. The design procedure is entirely carried out in the state space framework, which facilitates the application of other analysis and design methods in this framework. First, the non-linear minimum variance (NMV) controller is introduced and then by changing the cost function, NGMV controller is defined as an extended version of the linear cases. The cost function is used in the simplest form and can be easily extended to the general case. Simulation results show the effectiveness of the proposed non-linear method.
A non-monotonic Lyapunov function (NMLF) is deployed to design a robust H2 fuzzy observer-based control problem for discrete-time nonlinear systems in the presence of parametric uncertainties. The uncertain nonlinear system is presented as a Takagi and Sugeno (T–S) fuzzy model with norm-bounded uncertainties. The states of the fuzzy system are estimated by a fuzzy observer and the control design is established based on a parallel distributed compensation scheme. In order to derive a sufficient condition to establish the global asymptotic stability of the proposed closed-loop fuzzy system, an NMLF is adopted and an upper bound on the quadratic cost function is provided. The existence of a robust H2 fuzzy observer-based controller is expressed as a sufficient condition in the form of linear matrix inequalities (LMIs) and a sub-optimal fuzzy observer-based controller in the sense of cost bound minimization is obtained by utilising the aforementioned LMI optimisation techniques. Finally, the effectiveness of the proposed scheme is shown through an example.
A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil-sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process.
In this study, seismic attributes have been used to estimate well logs in one of the Iranian petroleum reservoirs. Three static methods have been evaluated: the linear model, the multilayer perceptron (MLP) and the radial basis function (RBF). For linear case, the selection of appropriate attributes was determined by forward selection and for nonlinear one, the selection was based on the genetic algorithm (GA) result. Parameters of nonlinear models were determined by cross-validation and then well logs were estimated. By comparing estimated and actual logs, RBF has the best performance with least training error. Since well logs contain high frequency content, so localized networks such as RBF has better performance than MLP through the study data set.
Dynamic Matrix Control is a widely used Model Predictive Controller in industrial processes. The successful implementation of Dynamic Matrix Control in practical applications requires appropriate tuning of the controller parameters. Three different cases are considered. In the first case, a tuning formula is developed that ensures the nominal closed loop desired performance. However, this formula may fail in the presence of plant uncertainty. Therefore a lower bound for the tuning parameter is derived to secure the robust stability of the uncertain first order plus dead time plant. Finally, a tuning boundary is derived which gives the lower and upper permissible bounds for the tuning parameter that guarantee the robust performance of the uncertain first order plus dead time plant. The tuning procedure is based on the application of Analysis of Variance, curve fitting and nonlinear regression analysis. The derived results are validated via simulation studies and some experimental results.
The walking beam furnace is one of the most prominent process plants often met in an alloy steel production factory and characterised by high non-linearity, strong coupling, time delay, large time-constant and time variation in its parameter set and structure. From another viewpoint, the walking beam furnace is a distributed- parameter process in which the distribution of temperature is not uniform. Hence, this process plant has complicated non-linear dynamic equations that have not worked out yet. In this paper, we propose one-step non-linear predictive model for a real walking beam furnace using non-linear black-box subsystem identification based on locally linear neuro-fuzzy model. Furthermore, a multi-step predictive model with a precise long prediction horizon (i. e., ninety seconds ahead), developed with application of the sequential one-step predictive models, is also presented for the first time. The locally linear model tree which is a progressive tree-based algorithm trains these models. Comparing the performance of the one-step linear neuro-fuzzy model predictive models with their associated models obtained through least squares error solution proves that all operating zones of the walking beam furnace are of non-linear sub-systems. The recorded data from Iran Alloy Steel factory is utilized for identification and evaluation of the proposed neuro-fuzzy predictive models of the walking beam furnace process.
Successful implementation of predictive controller requires an appropriate tuning of its parameters. Closed form tuning equations are practically rewarding as they can be easily implemented with relatively low computational costs. In this paper, a tuning strategy for the generalized predictive control of single input-single output and multi input-multi output plants is presented. First order plus dead time model of the plant is considered and analysis of variance and nonlinear fitting is employed to derive tuning equations. Finally, simulation results are used to verify the efficiency of the proposed tuning strategy.
This paper addresses the problem of output (angular position) feedback tracking control of two-degree-of-freedom X–Y pedestal systems. Both the velocity observer and the controller are based on a partial quasi-linearized model for the X–Y pedestal system. The two-dimensional velocity observer is uniformly globally exponentially convergent and does not require a priori upper-bound knowledge of the velocity magnitude. An important feature of the proposed observer is that it constructs a uniform global stable output feedback tracking controller with any domain of initial tracking errors and initial estimation errors. The proof of the main results is based on the well-established theorems for cascaded nonlinear time-varying systems. Due to uniform asymptotic stability of the observer and the output feedback controller, numerical simulations show their robust performance in the face of bounded additive perturbations on both input and output.
In this paper, three strategies are analysed and compared for optimal determination of tyre friction forces used for vehicle lateral-plane motion control. The valueability of this determination depends on the feasibility of the solution of a real-time optimisation problem. In strategy (III), the optimisation problem is relaxed from the equality constraints (enforced in strategies (I) and (II)) posed owing to the stabilisation and tracking objectives of the closed loop and instead these objectives are included in the cost function of the optimisation problem. In this way, the problem of the existence of feasible solution encountered in strategy (II) is remedied without infringing the saturation restrictions imposed by the limited physical capability of the tyres and actuators in developing tyre friction forces, which was overlooked in strategy (I). Detailed simulation studies show convincing performance that can be achieved with strategy (III) in physical entire range of operation including mild, moderate and severe manoeuvre conditions.
An important feature in dynamic systems that model behavior in human society is the role of expectations formed by individual agents within such systems. Unlike physical system in engineering, social systems are inhabited by sentient decision makers that react to their environment and form expectations about future events and the decisions of other agents in the society. As a consequence, conventional tools used in engineering may not apply. However, a large body of contributions in the engineering literature to the field of intelligent systems may still be useful for the analysis of expectational dynamic social systems. This paper adapts an emerging literature on agentbased computational economics, exemplified in (LeBaron and Tesfatsion (2008); Oeffner (2008); Tesfatsion (2002 )), to the issue of deriving from-the-ground-up formulas of expectations useful for macro-economic analysis. It joins established optimization methods in economics with results in multi-agent predictive control from the engineering literature summarized in (Cao, Yu, Ren and Chen (2013); Maestre, Muñoz de la Peña and Camacho (2011 )). The benefit to this approach is that the resulting dynamic structures under individual optimization are causal and easily solved, unlike typical non-causal rational expectations models.
This paper presents a neutral system approach to the design of an H∞ controller for input delay systems in presence of uncertain time-invariant delay. It is shown that when proportional derivative (PD) controller is applied to a time-delay system, the resulting closed loop system is generally a time-delay system of neutral type with delay term coefficients depending on the controller parameters. A descriptor model transformation is used to derive an advantageous bounded real lemma representation for the system. Furthermore, new delay-dependent sufficient conditions for the existence of an H∞ PD and PI controller in presence of uncertain delay are derived in terms of matrix inequalities. Some case studies and numerical examples are given in order to illustrate the advantages of the proposed method.
This paper presents a novel procedure for classification of normal and abnormal operating conditions of a process when multiple noisy observation sequences are available. Continuous time signals are converted to discrete observations using the method of triangular representation. Since there is a large difference in the means and variances of the durations and magnitudes of the triangles at different operating modes, adaptive fuzzy membership functions are applied for discretization. The expectation maximization (EM) algorithm is used to obtain parameters of the different modes for the durations and magnitudes assuming that states transit to each other according to a Markov chain model. Applying Hamilton's filter, probability of each state given new duration and magnitude is calculated to weight the membership functions of each mode previously obtained from a fuzzy C-means clustering. After adaptive discretization step, having discrete observations available, the combinatorial method for training hidden Markov models (HMMs) with multiple observations is used for overall classification of the process. Application of the method is studied on both simulation and industrial case studies. The industrial case study is the detection of normal and abnormal process conditions in the primary separation vessel (PSV) of an oil sand industry. The method shows an overall good performance in detecting normal and risky operating conditions.
This paper presents a theoretical approach to implementation of the “Multi realization of nonlinear MIMO systems”. This method aims to find state variable realization for a set of systems, sharing as many parameters as possible. In this paper a special nonlinear multi- realization problem, namely the multirealization of feedback linearizable nonlinear systems is considered and an algorithm for achieving minimal stably based multirealization of a set of nonlinear feedback linearizable systems is introduced.An example that illustrates this algorithm is also presented.
Multivariable model predictive control is a widely used advanced process control methodology, where handling delays and constraints are its key features. However, successful implementation of model predictive control requires an appropriate tuning of the controller parameters. This paper proposes an analytical tuning approach to multivariable model predictive controllers. The considered multivariable plants are square and consist of first-order plus dead time transfer functions. Most of the existing model predictive control tuning methods are based on trial and error or numerical approaches. In the case of no active constraints, closed loop transfer function matrices are derived and decoupling conditions are addressed. For control horizon of one, analytical tuning equations and achievable performances are obtained. Finally, simulation results are used to verify the effectiveness of the proposed tuning strategy.
A chaotic oscillator based on the memristor is analyzed from a chaos theory viewpoint. Sensitivity to initial conditions is studied by considering a nonlinear model of the system, and also a new chaos analysis methodology based on the energy distribution is presented using the Discrete Wavelet Transform (DWT). Then, using Advance Design System (ADS) software, implementation of chaotic oscillator based on the memristor is considered. Simulation results are provided to show the main points of the paper.
Robustness of parameter estimator plays a vital role in adaptive controllers. A modified identification algorithm is proposed based on the augmented UD identification (AUDI) primary version. Augmented UD identification with selective forgetting (AUDSF) method is derived as a robust derivation of AUDI to be integrated with input-output data filtering, relative dead zone, and data normalisation features. AUDSF is incorporated by generalised predictive controller (GPC) strategy to produce an applicable adaptive control method. The comparative performances of the developed approach have been explored on two-mass spring challenging benchmark problem, which demonstrates its excellent behaviour under conducted parameter and disturbance uncertainty scenarios.
The dynamic feedback control of the cardiac pacing interval has been widely used to suppress alternans. In this paper, temporally and spatially suppressing the alternans for cardiac tissue consisting of a one-dimensional chain of cardiac units is investigated. The model employed is a nonlinear partial difference equation. The model's fixed points and their stability conditions are determined, and bifurcations and chaos phenomenon have been studied by numerical simulations. The main objective of this paper is to stabilize the unstable fixed point of the model. The proposed approach is nonlinear spatiotemporal delayed feedback, and the appropriate interval for controller feedback gain is calculated using the linear stability analysis. It is proven that the proposed approach is robust with respect to all bifurcation parameter variations. Also, set point tracking is achieved by employing delayed feedback with an integrator. Finally, simulation results are provided to show the effectiveness of the proposed methodology.
This paper presents a variable structure rule-based fuzzy control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic anti-control. Based on Lyapunov stability theory for variable structure control and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. The fuzzy rules are directly constructed subject to a Lyapunov function obtained from variable structure surfaces such that the error dynamics of control problem satisfy stability in the Lyapunov sense. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anticontrol aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible-joint manipulator.
Real time knowledge of total mass of gas and liquid in the annulus and geological properties of the reservoir is very useful in many active controllers, fault detection systems and safety applications in the well during petroleum exploration and production drilling. Sensors and instrumentation can not measure the total mass of gas and liquid in the well directly and they are computed by solving a series of nonlinear algebraic equations with measuring the choke pressure and the bottom-hole pressure. This paper presents different estimator algorithms for estimation of the annular mass of gas and liquid, and production constants of gas and liquid from the reservoir into the well during Under Balanced Drilling. The results show that all estimators are capable of identifying the production constants of gas and liquid from the reservoir into the well, while the Lyapunov based adaptive observer gives the best performance comparing with other methods when there is a significant amount of noise.
In this paper, a simple method is presented for tuning weighted PIλ + Dμcontroller parameters based on the pole placement controller of pseudo-second-order fractional systems. One of the advantages of this controller is capability of reducing the disturbance effects and improving response to input, simultaneously. In the following sections, the performance of this controller is evaluated experimentally to control the vertical magnetic flux in Damavand tokamak. For this work, at first a fractional order model is identified using output-error technique in time domain. For various practical experiments, having desired time responses for magnetic flux in Damavand tokamak, is vital. To approach this, at first the desired closed loop reference models are obtained based on generalized characteristic ratio assignment method in fractional order systems. After that, for the identified model, a set-point weighting PIλ + Dμcontroller is designed and simulated. Finally, this controller is implemented on digital signal processor control system of the plant to fast/slow control of magnetic flux. The practical results show appropriate performance of this controller.
This paper addresses the problem of stabilizing a TORA system without velocity measurement. For this purpose, two classes of output feedback designs, direct and indirect, are employed to design a nonlinear observer for estimating an unavailable variable (velocity variable). Moreover, the theory of cascaded time-varying systems has been used to improve the indirect output feedback controller and to enable the independent tuning of the observer and the controller. The results of Lyapunov stability analysis show globally asymptotic stability of the system in closed loop using the output feedback controllers designed in this paper.
In this paper, evolutionary algorithms are proposed to compute the optimal parameters of Gaussian Radial Basis Adaptive Backstepping Control (GRBABC) for chaotic systems. Generally, parameters are chosen arbitrarily, so in several cases this choice can be tedious. Also, stability cannot be achieved when the parameters are inappropriately chosen. The optimal design problems are to introduce optimization algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO) in order to find the optimal parameters which minimize a cost function defined as an error quadratic function. These methods are applied to two chaotic systems; Duffing Oscillator and Lü systems. Simulation results verify that our proposed algorithms can achieve enhanced tracking performance regarding similar methods.
In the presence of plant uncertainties, utilizing an appropriate controller for a smooth output tracking and elimination of high-frequency disturbances, especially in accurate systems is very important. In this paper, a controller is proposed based on the robust and optimal theory to achieve a combination of such characteristics in the face of model parameter variations and unknown disturbances. The proposed controller has been simulated on a three-axis gyro-stabilized MIMO platform and comparison results with a NLPID controller simulation are provided.
Electro-hydrostatic actuator (EHA) is a kind of hydraulic system in which fluid is routed directly by pump to the actuator. In this study, a novel adaptive fuzzy-PID controller is developed to improve position controlling performance of an EHA. First of all, design and simulation of an EHA by using multidisciplinary modelling method is presented. This model is evaluated by soft validation method. The whole proposed novel control system is composed of a pair of interconnected subsystems, that is, a simple fuzzy-PID controller (SFPID) and a radial basis function neural network (RBFNN) to enhance the tracking performance. The RBFNN fuzzy-PID control (RBFNNF-PID) is applied to EHA. Also, SFPID control, fuzzy-PID control based on extended Kalman filter using grey predictor (FPIDKG) and simple adaptive control (SAC) as significant controls are applied to EHA. The simulation results have shown a significant improvement in transient response and reduction in sum square error (SSE).
This study presents the normative knowledge source for the belief space of cultural algorithm(CA) based on an adaptive Radial Basis Function Neural Network (RBFNN). The use of the RBFNN makes it possible to use the previous upper and lower bounds of the normative knowledge to update them and to extract a logical relationship between the previous parameters of the normative knowledge and their new values. The proposed algorithm(N3KCA) is similar to what the human brain does, i.e. to predict the new values of the bounds of normative knowledge based on the previous ones and some knowledge, which it has gained from the previous successive updates. Finally, the proposed cultural algorithm is evaluated on 10 unimodal and multimodal benchmark functions. The algorithm is compared with several other optimization algorithms including previous version of cultural algorithm. In order to have a fair comparison, the number of cost function evaluation is kept the same for all optimization algorithms. The obtained results show that the proposed modification enhances the performance of the CA in terms of convergence speed and global optimality.
It is not possible to directly measure the total mass of gas and liquid in the annulus and geological properties of the reservoir during petroleum exploration and production drilling. Therefore, these parameters and states must be estimated by online estimators with proper measurements. This paper describes a nonlinear Moving Horizon Observer to estimate the annular mass of gas and liquid, and production constants of gas and liquid from the reservoir into the well during Under-Balanced Drilling with measuring the choke pressure and the bottom-hole pressure. This observer algorithm based on a low-order lumped model is evaluated against Joint Unscented Kalman filter for two different simulations with low and high measurement noise covariance. The results show that both algorithms are capable of identifying the production constants of gas and liquid from the reservoir into the well, while the nonlinear Moving Horizon Observer achieves better performance than the Unscented Kalman filter.
This paper presents a new approach for the stability analysis and controller synthesis of discrete-time Takagi-Sugeno fuzzy dynamic systems. In this paper, nonmonotonic Lyapunov function is utilized to relax the monotonic requirement of Lyapunov theorem which renders larger class of functions to provide stability. To this end, three new sufficient conditions are proposed to establish global asymptotic stability. In this regard, the Lyapunov function decreases every few steps; however, it can be increased locally. Moreover, a new method is proposed to design the state feedback controller. It is shown that the Lyapunov function and the state feedback control law can be obtained by solving a set of Linear Matrix Inequalities (LMI) or Iterative Linear Matrix Inequalities (ILMI) which are numerically feasible with commercially available softwares. Finally, the exhausted numerical examples manifest the effectiveness of our proposed approach and that it is less conservative compared with the available schemes.
In this paper, based on the nonmonotonic Lyapunov functions, a new less conservative state feedback controller synthesis method is proposed for a class of discrete time nonlinear systems represented by Takagi-Sugeno (T-S) fuzzy systems. Parallel distributed compensation (PDC) state feedback is employed as the controller structure. Also, a T-S fuzzy observer is designed in a manner similar to state feedback controller design. The observer and the controller can be obtained separately and then combined together to form an output feedback controller by means of the Separation theorem. Both observer and controller are obtained via solving a sequence of linear matrix inequalities. Nonmonotonic Lyapunov method allows the design of controllers for the aforementioned systems where other methods fail. Illustrative examples are presented which show how the proposed method outperforms other methods such as common quadratic, piecewise or non quadratic Lyapunov functions.
Prediction of seasonal influenza epidemics is certainly a forming and effective step towards taking appropriate preventive actions. Improvement on public informing, decreasing the number of infected cases, undesirable effects and deaths due to influenza and also increasing vigilance of Iranian Influenza Surveillance System (IISS), have been practical goals of this research. A forecasting system has been designed and developed using Artificial Neural Networks (ANNs). It is a novel research as a novel dataset has been exploited. The data are categorized in two groups of climatic parameters (temperature, humidity, precipitation, wind speed & sea level pressure) and number of patients (number of total referrals and number of patients with Influenza-Like Illnesses (ILI)). In order to evaluate the model performance, different cost functions are defined and results indicate that the model provides the possibility of a satisfactory forecasting and is practically helpful to achieve the objectives already claimed.
A real-time dynamic hardware-in-loop (HIL) simulator of an RTX real-time subsystem (RTSS) was developed by using LabVIEW (G language). The main idea of this work was to determine the feasibility and accuracy of widely available and highly competitive commercial products, such as personal computers on an RTSS, as an alternative to conventional prohibitive real-time simulators in dynamic studies of power systems. The implemented system is a self-contained heavy-duty gas turbine, governor, synchronous 200-MVA, 15.75-kV machine and a simplified electrical network. The HIL simulator was customized to interact with a 1518-kW static exciter. The role of this HIL simulator is to provide real-time digital and analog signals for static exciter systems (SES) and to simulate the mechanical and electrical components in a closed-loop, fixed-step solver applied by a well-known numerical solution method. This sophisticated yet exceptionally economic HIL simulator provides engineers with a safe environment to analyze the dynamic performance of static exciters and investigate their natural restraints and functionalities. It also provides a safe environment to analyze some naturally destructive tests.
Model Predictive Controllers (MPC) are effective control strategies widely used in the industry. The desirable MPC performance requires appropriate tuning of the controller parameters. However, the MPC tuning parameters are related to the closed loop characteristics in a complex and nonlinear manner, so the tuning procedure is an intricate problem, which has received much attention in recent decades. In this paper, the effects of each tuning parameter on the closed loop behavior are studied. Then, the issue of MPC tuning problem is considered and a review of the available tuning methods are provided. Modern tuning strategies are also considered. The emphasis of this paper is on theoretical tuning strategies which lead to closed form tuning equations that can be used in closed loop analysis. Finally, a simulation study is employed to have a comparative study on some closed form tuning equations and the advantages and disadvantages of each method is clarified.
In this paper, a robust second order sliding mode control (SMC) for controlling a quadrotor with uncertain parameters presented based on high order sliding mode control (HOSMC). A controller based on the HOSMC technique is designed for trajectory tracking of a quadrotor helicopter with considering motor dynamics. The main subsystems of quadrotor (i.e. position and attitude) stabilized using HOSMC method. The performance and effectiveness of the proposed controller are tested in a simulation study taking into account external disturbances with consider to motor dynamics. Simulation results show that the proposed controller eliminates the disturbance effect on the position and attitude subsystems efficiency that can be used in real time applications.
This paper presents a constrained finite horizon model predictive control (MPC) scheme for regulation of the annular pressure in a well during managed pressure drilling from a floating vessel subject to heave motion. In addition to the robustness of a controller, how to deal with heave disturbances despite uncertainties in the friction factor and bulk modulus is investigated. The stochastic model describing sea waves in the North Sea is used to simulate the heave disturbances. The results show that the closed-loop simulation without disturbance has a fast regulation response, without any overshoot, and is better than a proportional-integral-derivative controller. The constrained MPC for managed pressure drilling shows further improved disturbance rejection capabilities with measured or predicted heave disturbance. Monte Carlo simulations show that the constrained MPC has a good performance to regulate set point and attenuate the effect of heave disturbance in case of significant uncertainties in the well parameter values.
Modern systems are required to guarantee a high degree of safety and self-diagnostics capabilities. This paper investigates the problem of state fault diagnosis in nonlinear systems with modeling uncertainties. In contrast with common literature, the fault diagnosis scheme is proposed in discrete time domain. This property relaxes the risk of stability and performance degradation in deriving discrete equivalent of continuous methods. An estimator is designed in order to generate residual signal by utilizing a proper nonlinear state transformation. A robust compensator term is implemented in estimator to decrease effect of modeling uncertainties and approximation error on residual signal. When the residual signal is exceeded detection threshold, an on-line fault approximator is turned on and trained by appropriate parameter update law. An extra term is considered in update rule to overcome the need of persistency of excitation (PE). The implement of all robust compensator term, PE relaxing term and proper parameter adaption law improve the accuracy of fault reconstruction. The result would be obviously vital in tolerant and time-life prediction stages after fault diagnosis.
In this paper, an adaptive multiple model predictive controller (AMMPC) based on multiple model switching and tuning strategy and dynamic matrix control (DMC) system is presented to construct switching-tuning adaptive multiple model predictive controller (STAMMPC). Disadvantages of non adaptive multiple model predictive control (MMPC) in regulation and disturbance rejection are discussed and new robust adaptive supervisors to improve the decision making procedures are developed. Experimental results on pH neutralization process show that the proposed decentralized control strategy using STAMMPC algorithm has desirable performance and robustness characteristics and is superior to the other MMPC algorithms, especially in the case of the participation with suggested new adaptive disturbance rejection supervisor.
A relatively simple and exact solution of a control allocation algorithm with low computational cost can greatly influence a multivariable system performance. In this paper the pseudo inverse approach is used to achieve the exact answer. Then, the solution is modified by the null space of control matrix in order to satisfy the constraints. Therefore, we could hold the simplicity and exactness of the pseudo inverse approach and remove its deficiency by the proposed methodology. Furthermore, a simulation is provided to show the main characteristics of the proposed method and its superiority.
Yaw instability of automotive vehicles occurs dangerous accidents particularly while driving on wet or icy surfaces. Considering wet or icy situations as faults, fault tolerant controllers are suitable to handle the control of automotive vehicles. In order to have yaw stability and increasing maneuverability and safety of faulty systems, using control allocation methods are good choices. This paper proposes a control allocation method based on the pseudo inverse along the null space of the control matrix (PAN) to establish lateral stabilization in automotive vehicle.
Model predictive control (MPC) is an effective control strategy in the presence of system constraints. The successful implementation of MPC in practical applications requires appropriate tuning of the controller parameters. An analytical tuning strategy for MPC of first-order plus dead time (FOPDT) systems is presented when the constraints are inactive. The available tuning methods are generally based on the user's experience and experimental results. Some tuning methods lead to a complex optimisation problem that provides numerical results for the controller parameters. On the other hand, many industrial plants can be effectively described by FOPDT models, and this model is therefore used to derive analytical results for the MPC tuning in a pole placement framework. Then, the issues of closed-loop stability and possible achievable performance are addressed. In the case of no active constraints, it is shown that for the FOPDT models, control horizons subsequent to two do not improve the achievable performance and control horizon of two provides the maximum achievable performance. Then, MPC tuning for higher order plants approximated by FOPDT models is considered. Finally, simulation results are employed to show the effectiveness of the proposed tuning formulas.
A multiple model structure of a prototype industrial gas turbine system is constructed under normal operation using a systematic method that incorporates non-linearity measure and H-gap metric tools with the multiple models technique. First, two new non-linearity indices for multiple input–multiple output systems are introduced and employed for decomposing the operating space of a gas turbine into some linear and non-linear modes. The non-linear modes may be further partitioned into some linear modes. The input and output data in each of the linear modes are used to construct an initial multiple model structure. In order to avoid the increase of the number of linear local models, the H-gap metric is extended to multiple input–multiple output systems and used to measure the similarity between linear local models and to merge the similar models. As a result, an algorithm is proposed for construction of multiple linear local models. The algorithm is employed for the identification of a single-shaft prototype industrial gas turbine.
This paper presents a constrained model predictive control scheme for regulation of the annular pressure in a well during managed pressure drilling from a floating rig subject to heave motion. The results show that closed-loop simulation without disturbance has a fast regulation response and without any overshoot. The robustness of controller to deal with heave disturbances is investigated. The constrained MPC shows good disturbance rejection capabilities. The simulation results show that this controller has better performance than a PID controller and is also capable of handling constraints of the system with the heave disturbance.
In this paper, Fault Detection and Isolation (FDI) is studied for the rotary kiln of Saveh White Cement Company. To do so, K-means algorithm as a crisp clustering, Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms as fuzzy clustering are used. In those, for finding number of clusters, Cluster Validity Indices (CVI) are applied. Principal Component Analysis (PCA) mapped the clusters into two dimensional spaces. Fault detection and isolation performance are evaluated by three criteria namely sensitivity, specificity, and confusion matrix. The results reveal that GK fuzzy algorithm provides better performance on detection and isolation of fault in this industrial plant.
An adaptive fault tolerant control systems are vital in many industrial systems. Redundancy is a practical approach to decrease the effects of faults in systems. Redundancy in actuators can also increase system reliability and flexibility. This paper proposes a fuzzy control allocation method that can allocate control signal among actuators to increase reliability and maneuverability in healthy conditions and tolerating faults in faulty conditions. Using fuzzy logic is an intelligent way to adaptively change the gains of control allocation in different operating conditions.
In this study, fault tolerant control for a Rotary Inverted Pendulum (RIP) has been improved by using chaos synchronization with adding a chaotic signal as a reference. Rotary inverted pendulum is a nonlinear, under-actuated, unstable and non-minimum-phase system. The proposed control consists of a state-feedback (LQR) and a fuzzy-PID control. The state- feedback control is used to stabilize system near the operating point, and the fuzzy-PID is used to track the chaos signal. PID controller gains adjust by fuzzy rule. The designed controller is implemented on a Quanser laboratory system.
Control of pH neutralization process has always been one of challenging problem in process control. The method presented here to control this process is the fuzzy identification of systems using Wiener model, and then multiplying the measured signal by the inverse of the nonlinear part of model. Therefore, we can design a linear controller for this new augmented system. This strategy is implemented in a generalized predictive control. One of the advantages of this control structure is consideration of explicit constraint in control of systems which also included in proposed fuzzy predictive control. At the end, the proposed method is tested on the model of a pH neutralization process.
MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.
In this study, first by using the collected real data from a 10000 cubic-meter Qazvin-kowsar water supply reservoir is modeled by nonlinear output error (NOE) structure, then a neural nonlinear controller based on the MLP neural network according to created model is designed in order to control the tank water level. The operation of the proposed controller is compared by a PID controller which its coefficients is optimized by genetic algorithm. Results of the simulation indicates that the neural nonlinear controller has a better function than the PID controller, and also this controller is able to control the level water of the tank appropriately regardless the consumer profile in all conditions even in consumer picks.
One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part of the process, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and non-linear dynamic equations. These equations have not completely extracted yet. Even in special cases, however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we present non-linear predictor and simulator models for a real cement rotary kiln by using non-linear identification technique on the locally linear neuro-fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise 15-minute horizon prediction for a cement rotary kiln are presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we present the pros and cons of these models. The data collected from White Saveh Cement Company is used for modelling.
This brief presents a X–Y pedestal using the feedback error learning (FEL) controller with adaptive neural network for low earth orbit (LEO) satellite tracking applications. The aim of the FEL is to derive the inverse dynamic model of the X–Y pedestal. In this brief, the kinematics of X–Y pedestal is obtained. To minimize or eliminate the backlash between gears, an antibacklash gearing system with dual-drive technique is used. The X–Y pedestal is implemented and the experimental results are obtained. They verify the obtained kinematics of the X–Y pedestal, its ability to minimize backlash, and the reduction of the tracking error for LEO satellite tracking in the typical NOAA19 weather satellite. Finally, the experimental results are plotted.
In this study it is attempted to describe the structure and procedure of training for the Interval Type-2 Fuzzy Logic inference System completely. To achieve this goal Adaptive Network- based Fuzzy Inference System (ANFIS) structure has been generalized to interval type-2 fuzzy, also all of the relations to describe inference structure and all of the necessary differentiation to adjust parameters with Gradient descent and Levenberg-Marquardt method has been brought. Described structure has been used to forecast Mackey-Glass chaotic time- series that polluted with additive uncertain domain noise. Using mentioned procedure for parameters adjustment achieved acceptable results.
This study proposes a novel chaotic anti-control for flexible joint system. The proposed controller is composed of a Lyapunov rule-based fuzzy control and chaotic anti-control for target tracking of the flexible joint manipulator. Chaotic signal is used to study the effect of anti-control to reduce the deflection of flexible joint system and control signal energy. For this purposes the flexible joint has been synchronized with chaotic Lorenz system. In this study on of the Lorenz parameters is changed to analysis the effect of chaotic signals. The results of the proposed approach shows in terms of level of vibration reduction and energy consumption of control signal, we could find an optimum point based on value of Lorenz system parameter. Finally, the efficacy of the proposed method and results of existence of different nonlinearity behavior is validated through experiments on QUANSER's flexible-joint manipulator.
Neural network based controller is used for controlling a mobile robot system. Feedback error learning (FEL) can be regarded as a hybrid control to guarantee stability of control approach. This paper presents simulation of a mobile robot system controlled by a FEL neural network and PD controllers. This feedback error-learning controller benefits from both classic and adaptive controller properties. The simulation results demonstrate that this method is more feasible and effective for mobile robot system control.
In this study interval type-2 fuzzy systems with non-singleton type-2 fuzzifire are used for identification and modeling nonlinear systems having noise with changing domain for fault detection purpose. The main idea in this fault detection method is to serve an upper bound and a lower bound as a confidence bound for system output that obtained from the interval type-2 fuzzy system. If we haven't precise information about mean and variance of noise, then non-singleton type-2 fuzzifire is usable. This fuzzifire improves performance of fault detection confidence bound. In the end of this paper a well-known benchmark two-tank system has been used for representing the advantages of proposed fault detection method.
In this paper, modeling, identification and control of a real 162MW heavy duty industrial gas turbine is taken into account. This work is aimed to introduce a simple and comprehensive model to test various controllers. Rowen's model is used to present the mechanical behavior of the gas turbine, while the identification of it is done using a feedforward neural network. The control rules of the turbine are applied on both models and a comparison of the results is also presented.
This paper proposes the modified projective synchronization method for unknown chaotic dissipative gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the dissipative gyroscope system, the system exhibits chaotic motions. As chaotic signals are usually broadband and noise-like, synchronized chaotic systems can be used as cipher generators for secure communication. Obviously the importance of obtaining these objectives is specified when the dynamics of the gyroscope system are unknown. In this paper, using the neural variable structure control technique, control laws are established, which guarantees the modified projective synchronization of an unknown chaotic dissipative gyroscope system. Switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. In the neural variable structure control, Gaussian radial basis functions are utilized online to estimate the system dynamic functions. Also, the adaptation laws of the online estimators are derived in the sense of Lyapunov function. Thus, the unknown chaotic gyroscope system can be guaranteed to be asymptotically stable. Also, the synchronization objectives have been achieved. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigenvalues of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for modified projective synchronization of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. The designed control system is robust versus model uncertainty. Numerical simulations are presented to verify the proposed synchronization method.
This paper deals with the issue of position control of an Electro-Hydrostatic Actuator (EHA) using an adaptive PID controller based on neurofuzzy network. In this relation, the design and simulation of an electro-hydrostatic actuation system referred to as EHA using multidisciplinary modeling method is presented. In recent years, fuzzy-PID controller is one of the main controllers that apply to the EHA systems. To improve the response of this controller, another control technique is needed to combine with the fuzzy-PID, and also, training some parameters of fuzzy-PID technique is a solution. The whole of new controller is composed of pair of interconnected subsystems, that is, an RBF network and conventional fuzzy-PID controller to enhance the tracking performance. Results show a significant improvement in transient response is achieved in comparison with a conventional fuzzy-PID control.
In this paper, obtaining of maximum active and reactive output power for wind turbines equipped with a double fed induction generator using stator-flux-oriented vector control based on novel multivariable input output linearization sliding mode control presented. The main control problem is the estimation of maximum power operating points of wind turbine under stochastic wind velocity profiles and tracking them using conventional offline and innovative adaptive online method. In this control strategy the wind speed and consequent aerodynamics torque is considered as the disturbance. Results under different operating conditions show the superior performance of the proposed online input-output linearization sliding mode technique.
This paper studies identification of a process in both frequent and infrequent operating points to design a nonlinear model predictive controller. Although, many of industrial processes normally work around an operating point, however they should seldom work in some infrequent points as well. In this case, due to low ratio of data points, identification of the processes based on all data set results in poor identification of the infrequent operating points. To resolve this problem, in this paper, at the first step, a data clustering strategy is used to group the data in different operating points. Since the ratio of infrequent to frequent data points is extremely low, the strategy used is the fuzzy Gath-Geva clustering methodology. Then, at the second step, a new approach has been proposed to compromise performance of identification of the nonlinear model for frequent and infrequent operating points. It is shown that if the ratio of data associated with frequent operating point to data of infrequent operating point is appropriately selected, the performance of the model remains satisfactory in the frequent operating point while the performance in the infrequent operating point is significantly improved as well. The proposed method gives an interval for appropriate ratio of data set in the highly nonlinear pH neutralization process.
This paper concerns application of data-derived approaches for analyzing and monitoring chemical process instruments, extracting product information, and designing estimation models for primary process variables, or difficult to measure in real-time variables. Modeling of process with an optimized classical neural network, the multi-layer perceptron (MLP) is discussed. Tennessee Eastman Process, a well-known plant wide process benchmark, is applied to validate the proposed approach. Investigations and several algorithms as step response test, Lipschitz number method and forward selection are used. The main advancement introduced here is that a hierarchical level responsible strategy is applied for selection of input variables and respective efficient time delays to attain the highest possible prediction accuracy of the neural network model for industrial process identification.
Many successful methods in various vision tasks rely on statistical analysis of visual patterns. However, we are interested in covering the gap between the underlying mathematical representation of the visual patterns and their statistics. With this general trend, in this paper a relationship between phase structure of a class of patterns and their moments after and before filtering have been considered. First, a general formula between the phase structure and moments of the images is obtained. Second, a theorem is developed that states under which conditions two visual patterns with the same frequencies but different phases have the same moments up to a certain moment. Finally, a theorem is developed that explains, given a set of filters, under which conditions two visual patterns with both different frequencies and different phases have the same subband statistics.
It has been known that, real right half plane (RHP) zeros imply serious limitations on the performance of nonminimum phase systems. Feedback cannot remove these limitations, mainly because RHP zeros cannot be cancelled by unstable poles of the controller since such a cancellation leads to internal instability. Hence, the idea of using fractional order systems in partial cancellation of the RHP zeros without leading to internal instability is studied. In this paper, the partial cancellation of RHP zeros with RHP poles is proposed using the fractional calculus approach. It is shown that undershoot and settling time of the compensated system is improved. Using suitable optimum criterion, it is shown that the performance of closed loop system can be relatively improved. Simulation results are used to show the effectiveness of the proposed methodology.
In this study, a new adaptive controller is proposed for position control of pneumatic systems. Difficulties associated with the mathematical model of the system in addition to the instability caused by Pulse Width Modulation (PWM) in the learning-based controllers using gradient descent, motivate the development of a new approach for PWM pneumatics. In this study, two modified Feedback Error Learning (FEL) methods are suggested and the their effectiveness are validated by experimental tracking data. The first one is a combination of PD (Proportionalâ Derivative) and RBF (Radial Basis Function) and in the second one RBF is replaced by ANFIS (Adaptive Neuro-Fuzzy Inference System). The robustness to varying mass is also examined. The experimental results show that the proposed algorithms, especially with ANFIS, are able to give good performance regardless of any uncertainties.
To improve the performance of a robust control, in presence of internal or external disturbance and uncertainties, to make a smooth tracking and elimination of high frequency disturbances especially in accurate systems with minimum power consumption an integration of robust optimal controller considered. Here, derivation and implementation of the proposed controller based on the combination of and controllers to use their characteristics against unknown disturbances is considered. The proposed controller was implemented on a 3 axis gyro-stabilized MIMO platform. The results which express the control designer desires, compared to the implemented NLPID and a single controller on the same system.
In a physical system several targets are normally being considered in which each one of nominal and robust performance has their own strengths and weaknesses. In nominal performance case, system operation without uncertainty has decisive effect on the operation of system, whereas in robust performance one, operation with uncertainty will be considered. The purpose of this paper is a balance between nominal and robust performance of the state feedback. The new approach of present paper is the combination of two controllers of μ and H2/H∞. The controller for robust stability status, nominal performance, robust performance and noise rejection are designed simultaneously. The controller will be achieved by solving constraint optimization problem. The paper uses a simultaneous H2/H∞/µ robust multivariable controller design over an X-29 Single Person aircraft. This model has three inputs and three outputs, where the state space equations of the system correspond to an unstable one. Simulation results show the effectiveness and benefits of the method.
In this paper, for a class of linear systems with unknown parameters, a direct model reference adaptive control scheme in output feedback form has been presented, which assures stable adaptation in the presence of input saturation. Also, under certain assumptions one can guarantee that the adaptive control signal will avoid input saturation. In addition, by considering that the error model is in a parametric model form, robust adaptive control is used to improve robustness of systems in the presence of bounded disturbances. This is achieved by using the a-modification method. Simulation of an output feedback system with relative degree 2 verifies the results given in the paper.
With respect to weight, energy consumption, and cost constraints, hydro-active suspension system is a suitable choice for improving vehicle ride comfort while keeping its handling. The aim of sensors selection is determining number, location, and type of sensors, which are the best for control purposes. Selection of sensors is related to the selection of measured variables (outputs). Outputs selection may limit performance and also affect reliability and complexity of control systems. In the meanwhile, hardware, implementation, maintenance, and repairing costs can be affected by this issue. In this study, systematic methods for selecting the viable outputs for hydro-active suspension system of a passenger car are implemented. Having joint robust stability and nominal performance of the closed loop is the main idea in this selection. In addition, it is very important to use these methods as a complementation for system physical insights, not supersedes. So, in the first place the system is described and the main ideas in ride comfort control are addressed. An 8 degrees of freedom model of vehicle with passive suspension system is derived and validated. Both linear and nonlinear models of the car which is equipped with hydro-active subsystem are derived. After selecting the outputs, for benefiting from minimum loop interactions, the control configuration is systematically determined. The main goal of selecting control configuration is assessing the possibility of achieving a decentralized control configuration. Finally, the system behavior is controlled by a decentralized proportional–integral–differential (PID) controller. The results indicate the efficiency of the controlled hydro-active suspension system in comparison with the passive system.
Information signal from real case and natural complex dynamical systems such as traffic flow are usually specified by irregular motions. Chaotic nonlinear dynamics approach is now the most powerful tool for scientists to deal with complexities in real cases, and neural networks and neuro-fuzzy models are widely used for their capabilities in nonlinear modeling of chaotic systems more than the traditional methods. As mentioned, the traffic flow conditions caused the forecasting values of traffic flow to lack robustness and accuracy. In this paper, the traffic flow forecasting is analyzed with emotional concepts and multi-agent systems (MASs) points of view as a new method in this field. The findings enabled the researchers to develop a newly object-oriented method of forecasting traffic flow. Its architecture is based on a temporal difference (TD) Q-learning with a neuro-fuzzy structure, which is the nonparametric approach. The performance of TD Q-learning is improved by emotional learning. The proposed method on the present conditions and the action of the system according to the criteria could forecast traffic signals so that the objectives are reached in minimum time. The ability of presented learning algorithm to prospect gains from future actions and obtain rewards from its past experiences allows emotional TD Q-learning algorithm to improve its decisions for the best possible actions. In addition, to study in a more practical situation, the neuro-fuzzy behaviors could be modeled by MAS. The proposed method (intelligent/nonparametric approach) is compared by parametric approach, autoregressive integrated moving average (ARIMA) method, which is implemented by multi-layer perceptron neural networks and called ARIMANN. Here, the ARIMANN is updated by backpropagation and temporal difference backpropagation for the first time. The simulation results revealed that the studied forecaster could discover the optimal forecasting by means of the Q-learning algorithm. Difficult to handle through parametric and classic methods, the real traffic flow signals used for fitting the algorithms is obtained from a two-lane street I-494 in Minnesota City.
MR-based methods have acceded an important role for the clinical detection and diagnosis of breast cancer. Dynamic contrast-enhanced MRI of the breast has become a robust and successful method, especially for the diagnosis of high-risk cases due to its higher sensitivity compared to X-ray mammography. In the clinical setting, the ANN has been widely applied in breast cancer diagnosis using a subjective impression of different features based on defined criteria. In this study, several neural networks classifiers like MLP, PNN, GRNN, and RBF has been presented on a total of 112 histopathologically verified breast lesions to classify into benign and malignant groups. Also, support vector machine has been considered as classifier. Before applying classification methods, feature selection has been utilized to choose the significant features for classification. Finally, to improve the performance of classification, three classifiers that have the best results among all applied methods have been combined together that they been named as multi-classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results have been achieved from two classifiers of multi-classifier system. Tables of results show that the proposed methods are correctly capable to feature selection and improve classification of breast cancer.
This study applies technique PDC (parallel distributed compensation) for speed control of a Digital Servo System. PDC method is based on nonlinear Takagi-Sugeno (TS) fuzzy model. Also in this study Neural Adaptive is used for velocity control and identification of a Digital Servo System. It is shown that these techniques can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by experimental and computer simulation. The controllers which introduced have big range for control the system. We compare PDC controller with Neural Adaptive controller results and PID controller.
In this paper, a new steganalysis method based on Cellular Automata Transform (CAT) is presented. CAT is used for feature extraction from stego and clean images. For that purpose, three levels CAT is applied on images and 12 sub-bands are generated for feature extraction. With adding the original image, 13 sub-bands are be used in feature extraction phase. In the next step, three moments of characteristic function (CF) are used as feature vector for every image (stego or clean image). At the end, Neural Network (NN) is applied as classifier. This supervised learning method uses these features for classifying the input image into either stego-image or clean-image. The performance of this algorithm is verified using some test samples. The results of our empirical tests show that detection accuracy of our method reaches to 93% for breaking MB2 and 91% for breaking LSB. Therefore the proposed method is a blind steganalysis method that can be used for broking some steganography methods.
Nonlinear behavior and disturbance sensitivity of the pH processes causes them to be known as an appropriate test bench for advanced controllers. Because of special behavior and varying parameters of pH processes, Multiple Model Predictive Controllers (MMPC) outperform other controllers from both regulation and disturbance rejection points of views. Two new supervisory methods based on prediction error and fuzzy weighting for MMPC are presented. Better regulation in special condition and most excellent disturbance rejection in comparison to other MMPC methods are achieved.
This brief proposes modified projective synchronization (MPS) methods for underactuated unknown heavy symmetric chaotic gyroscope systems via optimal Gaussian radial basis adaptive variable structure control. Chaotic gyroscope systems are considered as underactuated systems where a control input is designed to synchronize the two degree of freedoms interactions. Until now, no investigation of this subject with one control input has been presented. The importance of obtaining synchronization objectives is specified when the dynamics of gyroscope system are unknown. In this brief, using the neural variable structure control technique, a control law is established that guarantees the MPS of underactuated unknown chaotic gyros. In the neural variable structure control, Gaussian radial basis functions are utilized to estimate online the system dynamic functions. Adaptation laws of the online estimator are derived in the sense of the Lyapunov function. Moreover, online and offline optimizers are applied to optimize the energy of the control signal. The proposed solution is generalized to chaos control of the mentioned gyroscopes. Numerical simulations are presented to verify the proposed synchronization methods.
This paper shows a new fuzzy system was improved using genetic algorithm to handle fuzzy inference system as a function approximator and time series predictor. The system was developed generality that trained with genetic algorithms (GAs) corresponding to special problem and would be evaluated with different number of rules and membership functions. Then, compare the efficacy of variation of these two parameters in behavior of the system and show the method that achieves an efficient structure in both of them. Also, the proposed GA-Fuzzy inference system successfully predicts a benchmark problem and approximates an introduced function and results have been shown.
Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the primary issue here is to estimate splice sites precisely. In this paper, we have introduced two new methods in using neuro-fuzzy network and clustering for DNA splice site prediction. In this method, instead of using raw data and nucleotide sequence as an input to neural network, a survey on the first bunch of the nucleotide sequence of true and false categories of the input is carried out and training of the neuro-fuzzy network is achieved based on the similarities and dissimilarities of the selected sequences. In addition, sequences of the large input data are clustered into smaller categories to improve the prediction as they are really spliced based on different mechanisms. Experimental results show that these improvements have increased the recognition rate of the splice sites.
Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.
This paper presents the identification of V94.2 gas turbine. This turbine is built by Siemens. It has 162.1 MW nominal power and 50 Hz nominal frequency and is located at Kermanshah power plant, Kermanshah city of Iran. The stored data from turbine include fuel pressure valve angle and IGV1 angle as inputs and compressor output pressure, compressor output temperature, fuel pressure, turbine output power and turbine output temperature as outputs. To simplify identification process, the system turns into MISO2 systems to the number of outputs, and then correlation analysis is used to examine the dependence of the outputs to each input and other outputs. For turbine identification, dynamic linear models are estimated and then Feedforward neural network with one hidden layer is trained. The result shows dynamic linear models have poor performance in comparison with Feedforward neural network with one hidden layer. The neural network is able to identify a predictor model with fitness over 96% for outputs of V94.2 gas turbine.
In this paper, a new approach to the problem of stabilizing a chaotic system is presented. In this regard, stabilization is done by sustaining chaotic properties of the system. Sustaining the chaotic properties has been mentioned to be of importance in some areas such as biological systems. The problem of stabilizing a chaotic singularly perturbed system will be addressed and a solution will be proposed based on the OGY (Ott, Grebogi and Yorke) methodology. For the OGY control, Poincare section of the system is defined on its slow manifold. The multi-time scale property of the singularly perturbed system is exploited to control the Poincare map with the slow scale time. Slow scale time is adaptively estimated using a parameter estimation technique. Control with slow time scale circumvents the need to observe the states. With this strategy, the system remains chaotic and chaos identification is possible with online calculation of lyapunov exponents. Using this strategy on ecological system improves their control in three aspects. First that for ecological systems sustaining the dynamical property is important to survival of them. Second it removes the necessity of insertion of control action in each sample time. And third it introduces the sufficient time for census.
This paper proposes a novel approach for bilateral teleoperation systems with a multi degrees-of-freedom (DOF) nonlinear robotic system on the master and slave side with constant time delay in a communication channel. We extend the passivity based architecture to improve position and force tracking and consequently transparency in the face of offset in initial conditions, environmental contacts and unknown parameters such as friction coefficients. The proposed controller employs a stable neural network on each side to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitations of conventional controllers such as PD or adaptive controllers and guaranteeing good tracking performance. Moreover, we show that this new neural network controller preserves the control passivity of the system. Simulation results show that NN controller tracking performance is superior to that of conventional controllers.
In this paper input-output pairing is done based on concept of energy. Parseval theorem and cross-covariance samples of input-output are used for estimation of energy. After approximating interaction energy between input and output of the plant, input-output pairing is fulfilled. Through examples, it is illustrated that proposed method is appropriate for input-output pairing. The result is compared with Effective Relative Energy Array (EREA) as another energy based approach for input-output pairing.
In this paper, an indirect adaptive generalized predictive controller (GPC) is proposed by incorporating an augmented UD identifier (AUDI), based on Bierman's UD factorization algorithm. The developed adaptive control scheme is mainly aimed to deal with systems having linear time varying (LTV) dynamic characteristics. A series of simulation studies has been conducted to reveal the effectiveness of the developed adaptive control scheme to cope with such time varying dynamic profiles. The obtained results illustrate the controller robustness against both external disturbances and parameters uncertainties.
Despite active research and significant progress in the last three decades on control of human eye movements, it remains challenging issue due to its applications in prosthetic eyes and robotics. Till now, no considerable investigation of this subject is presented in the interdisciplinary sciences. The goal of this paper is to present a distinguished survey of existing literature on the intelligent control of the human eye movements system applied in a huggable pet-type robot as a biomechatronic system. In this study, the basic knowledge of human eye movements control is explained to show how the neural networks in the brainstem control the human eye movements. The geometry and model of human eye movements system are investigated and this system is considered as a nonlinear control system. The specified model may only be an academic exercise. It can have scientific importance in understanding of the human movement system in general. Also, it can be useful for robotics. Intelligent methods such as artificial neural networks and fuzzy neural networks are proposed to control the human eye movements and numerical simulations are presented. It is discussed that the intelligent controls applied to control of human eye movements system are emulated from the neural controls in biological system.
This paper addresses the experimental identification of a servo actuator which is used in many industrial applications. Because the system consisted of electrical and mechanical components, the behavior of the system was nonlinear. In addition, the under load behavior of this servo was different. The load torque was considered as the input and a two input-one output model was presented for this servo actuator. Special was given in order to present a simple and applicable model for this servo actuator. For identification of this servo actuator, classic and intelligent methods have been used. ARMAX model as a classic model and MLP and LOLIMOT networks as intelligent models were selected for this purpose and their results have been discussed. The comparisons between these methods show that the intelligent methods have a better accuracy than classical method, but they have more complexity in the implementation. These models can be applied as references for characterizing different designs and future control strategies.
The idea that chaos could be a useful tool for analyze nonlinear systems considered in this paper and for the first time the two time scale property of singularly perturbed systems is analyzed on chaotic attractor. The general idea introduced here is that the chaotic systems have orderly strange attractors in phase space and this orderly of the chaotic systems in subscription with other classes of systems can be used in analyses. Here the singularly perturbed systems are subscripted with chaotic systems. Two time scale property of system is addressed. Orderly of the chaotic attractor is used to analyze two time scale behavior in phase plane.
This paper presents a novel teleoperation controller for a nonlinear master–slave robotic system with constant time delay in communication channel. The proposed controller enables the teleoperation system to compensate human and environmental disturbances, while achieving master and slave position coordination in both free motion and contact situation. The current work basically extends the passivity based architecture upon the earlier work of Lee and Spong (2006)  to improve position tracking and consequently transparency in the face of disturbances and environmental contacts. The proposed controller employs a PID controller in each side to overcome some limitations of a PD controller and guarantee an improved performance. Moreover, by using Fourier transform and Parseval’s identity in the frequency domain, we demonstrate that this new PID controller preserves the passivity of the system. Simulation and semi-experimental results show that the PID controller tracking performance is superior to that of the PD controller tracking performance in slave/environmental contacts.
Breast cancer is the cause of the most common cancer death in women. Early detection of the breast cancer is an effective method to reduce mortality. Fuzzy Neural Networks (FNN) comprises an integration of the merits of neural and fuzzy approaches, enabling one to build more intelligent decision-making systems. But increasing the number of inputs causes exponential growth in the number of parameters in Fuzzy Neural Networks (FNN) and computational complexity increases accordingly. This phenomenon is named as “curse of dimensionality”. The Hierarchical Fuzzy Neural Network (HFNN) and the Fuzzy Gaussian Potential Neural Network (FGPNN) are utilized to deal this problem. In this study, the HFNN and FGPNN by using new training algorithm, are applied to the Wisconsin Breast Cancer Database to classify breast cancer into two groups; benign and malignant lesions. The HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. It can use fewer rules and parameters to model nonlinear system. Moreover, the FGPNN consists of Gaussian Potential Function (GPF) used in the antecedent as the membership function. When the number of inputs increases in FGPNN, the number of fuzzy rules does not increase. The performance of HFNN and FGPNN are evaluated and compared with FNN. Simulation results show the effectiveness of these methods even with less rules and parameters in performance result. These methods maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.
This paper proposes the chaos control and the generalized projective synchronization methods for heavy symmetric gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic motions of symmetric gyroscopes. In this paper, the switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. Using the neural variable structure control technique, control laws are established which guarantees the chaos control and the generalized projective synchronization of unknown gyroscope systems. In the neural variable structure control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimator are derived in the sense of Lyapunov function. Thus, the unknown gyro systems can be guaranteed to be asymptotically stable. Also, the proposed method can achieve the control objectives. Numerical simulations are presented to verify the proposed control and synchronization methods. Finally, the effectiveness of the proposed methods is discussed.
This paper proposes the chaos control and the modified projective synchronization methods for unknown heavy symmetric chaotic gyroscope systems via Gaussian radial basis adaptive backstepping control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a regular or periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise-like, synchronized chaotic systems can be used as cipher generators for secure communication. Obviously, the importance of obtaining these objectives is specified when the dynamics of gyroscope system are unknown. In this paper, using the neural backstepping control technique, control laws are established which guarantees the chaos control and the modified projective synchronization of unknown chaotic gyroscope system. In the neural backstepping control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimators are derived in the sense of Lyapunov function. Thus, the unknown chaotic gyroscope system can be guaranteed to be asymptotically stable. Also, the control objectives have been achieved. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigenvalues of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for modified projective synchronization of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Notice that it needs only one controller to realize modified projective synchronization no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. It seems that the proposed method can be useful for practical applications of chaotic gyroscope systems in the future. Numerical simulations are presented to verify the proposed control and synchronization methods.
This paper proposes mixed eigenstructure assignment with H∞ constraint when the states are not measurable. In this case, full state feedback is not permissible. So eigenstructure assignment by output feedback is considered. According to enhanced linear matrix inequality (LMI) and parametric eigenstructure assignment, we propose a method in terms of linear matrix inequality (LMI). This LMI can be easily solved by the Yalmip or LMI toolbox.
A novel structure of fuzzy logic controller is presented for trajectory tracking and vibration control of a flexible joint manipulator. The rule base of fuzzy controller is divided into two sections. Each section includes two variables. The variables of first section are the error of tip angular position and the error of deflection angle, while the variables of second section are derivatives of mentioned errors. Using these structures, it would be possible to reduce the number of rules. Advantages of proposed fuzzy logic are low computational complexity, high interpretability of rules, and convenience in fuzzy controller. Implementing of the fuzzy logic controller on Quanser flexible joint reveals efficiency of proposed controller. To show the efficiency of this method, the results are compared with LQR method. In this paper, experimental validation of proposed method is presented.
In this paper, the design of decentralized switching control for uncertain multivariable plants is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model and specific control structure. The underlying design is based on the quantitative feedback theory (QFT). It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local decentralized controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models’ behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down the switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input–output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Also, the multirealization technique is used to implement a family of controllers to achieve bumpless transfer. Simulation results are employed to show the effectiveness of the proposed method.
In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems in the design of a controller. This study presents a Takagi-Sugeno (TS)-type fuzzy system called a wavelet projection fuzzy inference system (WPFIS) in which a dimension reduction section is used at the input stage of the fuzzy system. In order to clarify the structure of the extracted features, structural learning with forgetting (SLF) based on Minkowski norms is proposed. In addition, gradient descent (GD) was used as a training algorithm. The results show that the proposed method has higher performance in comparison with conventional models. The data collected from Saveh White Cement Company were used in our simulations.
A helicopter has nonlinear dynamics and it'sa multivariable system. A helicopter is an unstable plant with high level of interaction between some of its variables. Therefore, controlling a helicopter is very difficult and to control it we must use special strategies. A fuzzy controller is a kind of nonlinear controller. It can control a plant without a mathematical model or a poor model. In this paper, we designed a fuzzy controller for an unmanned helicopter with finite degree of freedom. The fuzzy controller is designed based on optimal controller strategies. The proposed method has a better performance than state feedback optimal controller.
This paper presents energy reduction with anticontrol of chaos for nonholonomic mobile robot system. Anticontrol of chaos is also called chaotification, meaning to chaotify an originally non-chaotic system, and in this paper error of mobile robot system has been synchronized with chaotic gyroscope for reducing energy and increasing performance. The benefits of chaos synchronization with mechanical systems have led us to an innovation in this paper. The main purpose is that the control system in the presence of chaos work with lower control cost and control effort has been reduced. For comparison of proposed method, the feedback linearization controller has also been designed for mobile robot with noise. Finally, the efficacies of the proposed method have been illustrated by simulations, energy of control signals has been calculated, and effect of Alpha (: a constant coefficient is used beside of chaotic system) variations on the energy of control signals has been checked.
When the process is highly uncertain, even linear minimum phase systems must sacrifice desirable feedback control benefits to avoid an excessive ‘cost of feedback’, while preserving the robust stability. In this paper, the control structure of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed to control highly uncertain plants. According to this strategy, the uncertainty region is suitably divided into smaller regions. It is assumed that a QFT controller-prefilter exits for robust stability and robust performance of the individual uncertain sets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. Besides, each controller is designed to be stable in the whole uncertainty domain, and as accurate in command tracking as desired in its uncertainty subset to preserve the robust stability from any failure in the switching.
In this paper Fault Detection and Isolation (FDI) is shown as a pattern classification problem which can be solved using clustering techniques. Gath-Geva clustering (GGC) is exploited as optimal form by a performance assessment rule for fault detection, while multistage Gath-Geva clustering is employed for the intent of fault isolation. Furthermore since Visbreaker unit is a large scale process, a novel hybrid method on the basis of Principle Component Analysis and Genetic Algorithm optimization was also proposed in order to cope with the curse of dimensionality and complexity of computation problems. There are two main percentile criteria for validation of fault detection namely specificity and sensitivity. Evaluation of fault isolation has been depicted in confusion matrix. For analysis and visualization of the correlated high dimensional data, PCA maps the data point into lower dimensional space. The proposed FDI approaches have been evaluated through experimental Visbreaker process unit data collected in oil refinery.
This paper describes hybrid multivariate methods: Fisher's Discriminant Analysis and Principal Component Analysis improved by Genetic Algorithm. These methods are good techniques that have been used to detect faults during the operation of industrial processes. In this study, score and residual space of modified PCA and modified FDA are applied to the Tennessee Eastman Process simulator and show that modified PCA and modified FDA are more proficient than PCA and FDA for detecting faults.
Development of a fault detection scheme for nonlinear systems is often difficult due to complexity of the system. In this study a new method, based on parity relations for linear systems, is proposed to detect faults in nonlinear systems that can be modeled by Takagi- Sugeno (TS) fuzzy system. This method is an intuitive generalization of parity relations, because TS fuzzy system uses local linear models. Results of simulation and implementation on a rotary inverted pendulum show that faults can be detected very well.
This study presents fault tolerant control of inverted pendulum via on-line fuzzy backstepping and anti-control of chaos. The inverted pendulum is used frequently in robotic applications and can be found in different forms. Based on Lyapunov stability theory for backstepping design, the nonlinear controller and some generic sufficient conditions for asymptotic control are attained. Also in this study, anti-control of chaos is applied to increase the fault tolerant of inverted pendulum. To achieve this goal, the chaos dynamic must be created in the inverted pendulum system. So, the inverted pendulum system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of inverted pendulum system. The performances of the proposed control are examined in terms of fault tolerant capability. Finally, the efficacies of the proposed methods are illustrated by simulations.
In this paper, fault tolerant synchronization of chaotic gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control is investigated. Taking the general nature of faults in the slave system into account, a new synchronization scheme, namely, fault tolerant synchronization, is proposed, by which the synchronization can be achieved no matter whether the faults and disturbances occur or not. By making use of a slave observer and a Lyapunov rule-based fuzzy control, fault tolerant synchronization can be achieved. Two techniques are considered as control methods: classic Lyapunov-based control and Lyapunov rule-based fuzzy control. On the basis of Lyapunov stability theory and fuzzy rules, the nonlinear controller and some generic sufficient conditions for global asymptotic synchronization are obtained. The fuzzy rules are directly constructed subject to a common Lyapunov function such that the error dynamics of two identical chaotic motions of symmetric gyros satisfy stability in the Lyapunov sense. Two proposed methods are compared. The Lyapunov rule-based fuzzy control can compensate for the actuator faults and disturbances occurring in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method for fault tolerant synchronization.
Bounded rationally idea, rather that optimization idea, have result and better performance in decision making theory. Bounded rationality is the idea in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. The emotional theory is an important topic presented in this field. The new methods in the direction of purposeful forecasting issues, which are based on cognitive limitations, are presented in this study. The presented algorithms in this study are emphasizes to rectify the learning the peak points, to increase the forecasting accuracy, to decrease the computational time and comply the multi-object forecasting in the algorithms. The structure of the proposed algorithms is based on approximation of its current estimate according to previously learned estimates. The short term traffic flow forecasting is a real benchmark that has been studied in this area. Traffic flow is a good measure of traffic activity. The time-series data used for fitting the proposed models are obtained from a two lane street I-494 in Minnesota City, USA. The research discuss the strong points of new method based on neurofuzzy and limbic system structure such as Locally Linear Neurofuzzy network (LLNF) and Brain Emotional Learning Based Intelligent Controller (BELBIC) models against classical and other intelligent methods such as Radial Basis Function (RBF), Takagi–Sugeno (T–S) neurofuzzy, and Multi-Layer Perceptron (MLP), and the effect of noise on the performance of the models is also considered. Finally, findings confirmed the significance of structural brain modeling beyond the classical artificial neural networks.
Rotary kiln is the central and the most complex component of cement production process. It is used to convert calcineous raw meal into cement clinkers, which plays a key role in quality and quantity of the final produced cement. This system has complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedure, a large number of the involved parameters are crossed out and an approximation model is presented instead. Therefore, the performance of the obtained model is very important and an inaccurate model may cause many problems for designing a controller. This study presents hierarchical wavelet TS-type fuzzy inference system (HWFIS) for identification of cement rotary kiln. In the proposed method, wavelet fuzzy inference system (WFIS) with two input variables is used as sub-model in a hierarchical structure and gradient descent (GD) algorithm is chosen for training parameters of antecedent and conclusion parts of sub-models. The results show that the proposed method has higher performance in comparison with the other models. The data collected from Saveh White Cement Company is used in our simulations.
to enhance the closed loop performance in presence of disturbance, uncertainties and delay a double loop mixture of MPC and robust controller is proposed. This double loop controller ensures smooth tracking for a 3-axis gyro-stabilized platform which has delay intrinsically. This control idea is suggested to eliminate high frequency disturbances and minimize steady state error with minimum power consumption in simulation and experiment. Proposed controller based on the combination of H2 and H¥ controllers in the inner control loop shows the robustness of the proposed methodology. In the outer loop to have a good tracking performance, an integrated MPC is used to handle delay in system dynamics. Also, the main idea for dealing with uncertainties is using integral and derivative of platform attitude. In the proposed platform, the H¥ controller is compared with H¥/H2 controller in KNTU laboratory in theory and experiment. Results of experimental set up shows the same reaction of two controllers against disturbance and uncertainties in delayed system.
A new switching mechanism for multiple model adaptive controllers (MMAC) is suggested in this paper. The proposed method gives superior performance in comparison to the widely used method of switching based on performance function and hysteresis function in systems which experience high levels of measurement noise. This method acts as a complementary condition within the switching mechanism which checks the existence of excitation in system at every instant. The new method is evaluated by simulation studies on a nonlinear model of a pH neutralization plant.
Differential pipe sticking (DPS) is one of the most conventional and serious problems in drilling operations that imposes some extra costs to companies. This phenomenon originates mainly from improper mud properties, bottomhole assembly (BHA) (contacting area), still pipe time, and differential pressure between the formation and the drilling mud. Investigation on various conditions that lead to DPS makes it possible to develop some preventive treatments to avoid this problem's occurrence. In the past, statistical methods were applied in this area, but recently artificial neural network (ANN) approaches are frequently being used. ANNs have some priorities over conventional statistical methods such as the model-free form of predictions, tolerance to data errors, data-driven nature, and fast computation. On the other hand, the designed ANNs have some shortcomings and restrictions as they are developed to predict problems. In this paper, to solve most of the existing disadvantages of ANNs, a novel support-vector machine (SVM) approach has been developed to predict a DPS occurrence in horizontal and sidetracked wells in Iranian offshore oil fields. The results from the analysis have shown the potential of the SVM and ANNs to predict DPS, with the SVM results being more promising.
This study presents fault tolerant control of inverted pendulum via on-line fuzzy backstepping and anti-control of chaos. The inverted pendulum, as a mechatronics system, is used frequently in robotic applications and can be found in different forms. Based on Lyapunov stability theory for backstepping design, the nonlinear controller and some generic sufficient conditions for asymptotic control are attained. Also in this study, anti-control of chaos is applied to increase the fault tolerant of inverted pendulum. To achieve this goal, the chaos dynamic must be created in the inverted pendulum system. So, the inverted pendulum system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of inverted pendulum system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of fault tolerant capability. Finally the efficacy of the proposed methods are illustrated by simulations.
This paper presents a nonlinear control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic gyroscope synchronization. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. In this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is trying to design a controller which is capable to satisfy the control and anti-control aims. The performances of the proposed control are examined in terms of input tracking capability and level of vibration reduction. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible joint manipulator.
A combined approach for bumpless transfer multiple model predictive control (Multiple MPC) is proposed based on the Lyapunov function. State-space representation is used to design the controllers and the Lyapunov approach is employed to ensure closed loop stability. The proposed method uses both an intermediate controller and a bumpless mechanism in a unified configuration based on the stability analysis. Previous works on the bumpless multiple MPC design do not ensure closed loop stability, while the mechanism presented in this paper ensures both closed loop stability and applicable control performance for industrial processes. Finally, efficiency of the proposed method is validated by simulation results on non-isothermal continuous stirred-tank reactor (CSTR) system.
This study proposes a model-based robust fault detection and isolation (RFDI) method with hybrid structure. Robust detection and isolation of the realistic faults of an industrial gas turbine in steady-state conditions is mainly considered. For residual generation, a bank of time-delay multilayer perceptron (MLP) models is used, and in fault detection step, a passive approach based on model error modelling is employed to achieve threshold adaptation. To do so, local linear neuro-fuzzy (LLNF) modelling is utilised for constructing error-model to generate uncertainty interval upon the system output in order to make decision whether a fault occurred or not. This model is trained using local linear model tree (LOLIMOT) which is a progressive tree-construction algorithm. Simple thresholding is also used along with adaptive thresholding in fault detection phase for comparative purposes. Besides, another MLP neural network is utilised to isolate the faults. In order to show the effectiveness of proposed RFDI method, it was tested on a single-shaft industrial gas turbine prototype model and has been evaluated based on the gas turbine data. A brief comparative study with the related works done on this gas turbine benchmark is also provided to show the pros and cons of the presented RFDI method.
Minimal stopping distance, guaranteed steering ability and stability are the three most important purposes in Anti-lock Braking System (ABS) realm. The ABS system is a nonlinear, time variant and multivariable system with some uncertainties. Some research work has been carried out on ABS control systems using intricate methods which are expensive to implement. In this paper at the first step the system interference is decreased via decoupling matrix and the ABS is controlled with a robust diagonal controller. In fact, a decentralized control technique is used for our ABS control mechanism. At the second step we exploit a multivariable technique in linear control to attack the problem. This is the Designed Linear Control with Multivariable Technique. The Optimal Eigenstructure Assignment with Genetic Algorithm (GA) method is also applied. Simulation and comparison studies are used to show the effectiveness of the proposed methods.
Minimal stopping distance, guaranteed steering ability and stability are the three most important purposes in Anti-lock Braking System (ABS) realm. The ABS system is a nonlinear, time variant and multivariable system with some uncertainties. Some research work has been carried out on ABS control systems using intricate methods, which are expensive to implement. In this paper at the first step, the system interference is decreased via decoupling matrix and the ABS is controlled with a robust diagonal controller. In fact, a decentralized control technique is used for our ABS control mechanism. At the second step, we exploit a multivariable technique in linear control to attack the problem. This is the Designed Linear Control with Multivariable Technique. The Optimal Eigenstructure assignment with the Genetic Algorithm (GA) method is also applied. Simulation and comparison studies are used to show the effectiveness of the proposed methods.
This paper proposes the modified projective synchronization for heavy symmetric dissipative gyroscope systems via backstepping control. Because of the nonlinear terms of the gyroscope system, the system exhibits complex and chaotic motions. Using the backstepping control technique, control laws are established which guarantees the hybrid projective synchronization including synchronization, anti-synchronization and projective synchronization. By Lyapunov stability theory, control laws are proposed to ensure the stability of the controlled closed-loop. Numerical simulations are presented to verify the proposed synchronization approach. This paper demonstrates that synchronization and anti- synchronization can coexist in dissipative gyroscope systems via nonlinear control.
This paper presents a proposal for multiobjective Invasive Weed Optimization (IWO) based on nondominated sorting of the solutions. IWO is an ecologically inspired stochastic optimization algorithm which has shown successful results for global optimization. In the present work, performance of the proposed nondominated sorting IWO (NSIWO) algorithm is evaluated through a number of well-known benchmarks for multiobjective optimization. The simulation results of the test problems show that this algorithm is comparable with other multiobjective evolutionary algorithms and is also capable of finding better spread of solutions in some cases. Next, the proposed algorithm is employed to study the Pareto improvement model in two complex electricity markets. First, the Pareto improvement solution set is obtained for a three-player oligopolistic electricity market with a nonlinear demand function. Then, the IEEE 30-bus power system with transmission constraints is considered, and the Pareto improvement solutions are found for the model with deterministic cost functions. In addition, NSIWO algorithm is used to analyze this system with stochastic cost data in a risk management problem which maximizes the expected total profit but minimizes the profit risk in the market.
This paper provides a systematic method for model bank selection in multi-linear model analysis for nonlinear systems by presenting a new algorithm which incorporates a nonlinearity measure and a modified gap based metric. This algorithm is developed for off-line use, but can be implemented for on-line usage. Initially, the nonlinearity measure analysis based on the higher order statistic (HOS) and the linear cross correlation methods are used for decomposing the total operating space into several regions with linear models. The resulting linear models are then used to construct the primary model bank. In order to avoid unnecessary linear local models in the primary model bank, a gap based metric is introduced and applied in order to merge similar linear local models. In order to illustrate the usefulness of the proposed algorithm, two simulation examples are presented: a pH neutralization plant and a continuous stirred tank reactor (CSTR).
In this paper a new approach is proposed to design state feedback controllers for well known TS fuzzy systems. The used controller structure is in familiar parallel distributed compensation (PDC) format and the Lyapunov candidate is the frequently used common quadratic one. The only difference is the stability theorem used which is a revised version of Lyapunov stability theorem that relaxes monotonic condition. It is shown that the proposed method is less conservative than common quadratic method and piecewise method.
This paper presents a new approach for tuning PID controller parameters in the control of nonlinear systems. The design is based on optimal tracking of step response for nonlinear systems. The problem is first restated as a non linear optimal control infinite horizon problem, then with a suitable change of variable, the time interval is transferred to the finite horizon [0 1). This change of variable, poses a time varying problem. This problem is then transferred to measure space, and it is shown that an optimal measure must be determined which is equivalent to a linear programming problem with infinite dimension. Then, using finite horizon approximations, the optimal control law as piece wise constant function is determined. Finally, PID controller parameters are Determined using the optimal control law. Simulations are provided to show the effectiveness of the proposed methodology.
An output feedback model reference adaptive controller is developed for a class of linear systems with multiple unknown time-varying state delays and in the presence of actuator failures. The adaptive controller is designed based on SPR-Lyapunov approach and is robust with respect to multiple unknown time-varying plant delays and to an external disturbance with unknown bounds. Closed-loop system stability and asymptotic output tracking are proved using suitable Lyapunov-Krasovskii functional and Simulation results are provided to demonstrate the effectiveness of the proposed controller.
In this paper, a new methodology for robust controller design in nonlinear multivariable systems is suggested to guarantee asymptotic output tracking. The systems under consideration are perturbed by functionally bounded matched and unmatched uncertainties/perturbations and assumed to be described in the strict-feedback form. The main idea of the methodology is based on the combination of conventional sliding mode control and backstepping algorithm. The proposed controller called nested sliding mode controller that is obtained through a stepwise algorithm. It has the ability of rejecting nonvanishing perturbations by using dynamic switches, unlike conventional and other hierarchical sliding mode design methods. Performance is studied through theorems and verified by two numerical examples.
In this paper, a new practical robust water level control system for the U-tube steam generator (UTSG) using the quantitative feedback theory (QFT) is proposed. The steam generator is a nonlinear uncertain plant. However, the steam generator behaves as a linear uncertain and nonminimum phase plant at its different operating points, which makes its control a challenging problem. The control problem is to design controllers such that the closed-loop plant satisfies the robust stability, disturbance rejection, and robust tracking specifications that are derived from a desired steam generator performance. In the QFT design methodology, these specifications are satisfied by generating the plant templates, the composite bounds, and a nominal plant loop shaping procedure to satisfy these bounds. Simulation results reveal that the designed QFT water level controllers will ensure all the designers’ closed-loop specifications. Also, comparison results are provided that show the effectiveness of the robust QFT controllers with respect to the previously employed internal model-based controller.
In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H ∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.
The paper presents a new frequency-domain methodology to explicitly address the robustness margins for analysis and tuning of generalized predictive control (GPC). The GPC is formulated in two-degree-of-freedom configuration to allow for simultaneous execution of robustness analysis and frequency characteristic shaping. The underlying idea is to present a robust tuning scheme for GPC scheme by synthesizing some sensitivity functions in discrete-time domain, quantifying the relevant cause-and-effect perturbations, in order to shape them so that the effects of influences can be reduced in a specific frequency range. Several frequency-domain templates have been introduced to practically demonstrate usefulness of output, noise, and input sensitivity functions as complementing analysis tools for robust tuning of GPC. The proposed method ensures robust adjustments of the non-trivial tuning of GPC free parameter knobs through simultaneous realization of robustness analysis and frequency characteristic shaping. The method can hence be utilized as a powerful method for tuning of GPC for a wide range of single-input single-output (SISO) linear systems. Illustrative simulation examples have been conducted to explore the effectiveness of the proposed method.
In this paper, sliding mode control is utilized for stabilization of a particular class of nonlinear polytopic differential inclusion systems with fractional-order-0 < q < 1. This class of fractional order differential inclusion systems is used to model physical chaotic fractional order Chen and Lu systems. By defining a sliding surface with fractional integral formula, exploiting the concept of the state space norm, and obtaining sufficient conditions for stability of the sliding surface, a special feedback law is presented which enables the system states to reach the sliding surface and consequently creates a sliding mode control. Finally, simulation results are used to illustrate the effectiveness of the proposed method.
This paper presents the stabilization problem of a linear time invariant fractional order (LTI-FO) switched system with order 1< q< 2 by a single Lyapunov function whose derivative is negative and bounded by a quadratic function within the activation regions of each subsystem. The switching law is extracted based on the variable structure control with a sliding sector. First, a sufficient condition for the stability of an LTI-FO switched system with order 1< q< 2 based on the convex analysis and linear matrix inequality (LMI) is presented and proved. Then a single Lyapunov function, whose derivative is negative, is constructed based on the extremum seeking method. A sliding sector is designed for each subsystem of the LTI-FO switched system so that each state in the state space is inside at least one sliding sector with its corresponding subsystem, where the Lyapunov function found by the extremum seeking control is decreasing. Finally, a switching control law is designed to switch the LTI-FO switched system among subsystems to ensure the decrease of the Lyapunov function in the state space. Simulation results are given to show the effectiveness of the proposed VS controller.
This paper considers the pursuing or target tracking problem where an autonomous robotic vehicle is required to move towards a maneuvering target using range‐only measurements. We propose a switched logic‐based control strategy to solve the pursuing problem that can be described as comprising a continuous cycle of two distinct phases: (1) the determination of the bearing, and (2) the steering control of the vehicle to follow the direction computed in the previous step while the range is decreasing. We provide guaranteed conditions under which the switched closed‐loop system achieves convergence of the relative distance error to a small neighborhood around zero. Simulation results are presented and discussed.Copyright © 2011 John Wiley & Sons, Ltd.
Input-Output data modeling using multi layer perceptron networks (MLP) for a laboratory helicopter is presented in this paper. The behavior of the two degree-of-freedom platform exemplifies a high order unstable, nonlinear system with significant cross-coupling between pitch and yaw directional motions. This paper develops a practical algorithm for identifying nonlinear autoregressive model with exogenous inputs (NARX) and nonlinear output error model (NOE) through closed loop identification. In order to collect input-output identifier pairs, a cascade state feedback (CSF) controller is introduced to stabilize the helicopter and after that the procedure of system identification is proposed. The estimated models can be utilized for nonlinear flight simulation and control and fault detection studies.
Various studies have been devoted to modulation and control of power electronic systems. Modeling of such a system is often required for control purposes. One modeling approach is the standard state space average model (SSSAM), which considers switching behaviors of the converters. The developed SSSAM of the static compensators (STATCOM) describes a non-affine model that is hardly controllable. A decomposition procedure has been proposed in this paper to make this non-affine SSSAM like an affine model. First, a non-affine SSSAM is derived that includes an interconnected STATCOM to an equivalent Thevenin model of the network along with the load. Then, the proposed decomposition procedure is applied to the non-affine SSSAM, where the resultant affine SSSAM is simulated. Simulations are presented for both the non-affine and the proposed affine model, showing the performance of the proposed procedure.
Based on the Gas Path Analysis (GPA) method, nonlinear estimation and fuzzy classification theories, a comprehensive fault diagnosis system has been developed for an industrial Gas Turbine (GT). The hybrid method consists of two parts, in the first part noisy sensor output changes are translated to changes in the health parameters using an Extended Kalman Filter (EKF). In the second part the outputs of the EKF are used as the inputs of a fuzzy system. This system can isolate and evaluate the physical faults based on the predetermined rules obtained mostly from experimental data and aerothermodynamical simulations. The ratios of changes in different health parameters due to different faults and also the areas in the compressor most affected by these faults are the key factors for developing the rules. The Fuzzy Inference System (FIS) gives the fault locations in the compressor or turbine. Also, operator-friendly suggestions for the time of the compressor washing or components repair are provided. This leads to a hybrid fault detection and isolation solution for the GT, and with pre-filtering the data before use as input of fuzzy inference system, the accuracy of the fault diagnosis system is improved. Nonlinear simulation, estimation and classification results are provided to show the effectiveness of the proposed methodology.
Measuring the distance between two linear time invariant systems (LTIs) and its application are investigated by a very common and useful method (v-gap metric) and in following some related problems are raised. In many times we are interested in comparing two linear systems with their different frequency ranges and it is essence to have a metric which could cover all over frequency ranges of two plants. So by keeping the same topology of v-gap metric, a new metric is defined to measure distance between two linear systems. This metric is the extension of the v-gap metric on the linear systems from H∞ norm to H2 norm space. It is shown that all relations and equations which are used in v-gap can be proof in new space. The new metric not only has the ability of standard v-gap metric for measuring distance but also has some advantages. In addition by some simulations these two metrics are compared.
Expectation formation plays a principal role in economic systems. We examine and revise the standard rational expectations (RE) model, generally taken as the best paradigm for expectations modelling, and suggest a new method to model rational expectations. Conventional conditions that assert the stability and uniqueness of popular solution methods are shown to be insufficient. The agent-based new modelling approach suggested in this paper will be shown to lead to uniquely stable solutions.
In this paper we address the pursuing or target tracking problem where an autonomous robotic vehicle is required to move towards a maneuvering target using range-only measurements. A new switched based control strategy is proposed to solve the pursuing problem that can be described as comprising a continuous cycle of two distinct phases: i) the determination of the bearing, and ii) following the direction computed in the previous step while the range is decreasing. We provide conditions under which the switched closed-loop system achieves convergence of the relative distance error to a small neighborhood around zero. Simulation results are presented and discussed.
This paper proposes a novel architecture for bilateral teleoperation with a master and slave nonlinear robotic systems under constant communication delays. We basically extend the passivity based coordination architecture to improve position and force tracking and consequently transparency in the face of offset in initial conditions, environmental contacts and unknown parameters such as friction coefficient. This structure provides robust stability against constant delay and guarantee position and force tracking. The proposed controller employ a stable neural network in each side to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitation of adaptive control and guarantee good performance. An adaptation algorithm is developed to train the NN controller in order to stabilize the whole system. Furthermore, it is demonstrate that the tracking error of desired trajectory and NN weights are bounded. Simulation results show that NN controller tracking performance is superior to conventional coordination controller tracking performance.
In this paper a neural-fuzzy controller is used to control cement kiln. The fuzzy controller is in the TSK form. The controller is trained during the control action due to cope with the plant changes. The most important aspects of this controller are first using couple of smaller controllers instead of a complete centralized one and second using the same framework that kiln operators use. ie the input variables that the controller use are the same input variables that the kiln operators use to control the same controlled variables. Joint together, decentralized fuzzy controller instead of a centralized fuzzy one has fewer parameters which need less memory and processing power of the controller. The proposed controller is tested on a simulator model which made on the real data of Saveh cement factory. The simulation results show the efficiency of the proposed controller.
In this note, an adaptive observer is considered for simultaneous estimation of the states and unknown parameters of linear stationary systems with faulty measurements. Since entries of system matrices are functions of only a few parameters, it is enough to tune those parameters, instead of adapting all entries. This leads to reduced adaptation laws. Moreover, the problem of measurement offset and gain faults are also considered. The stability and convergence of the proposed adaptive observer is investigated. Simulation results validate the performance of the proposed adaptive observer.
This paper addresses the experimental identification of a servo actuator which is used in many industrial applications. Because the system consisted of electrical and mechanical components, the behavior of the system was nonlinear. In addition, the under load behavior of this servo was different. The load torque was considered as the input and a two input-one output model was presented for this servo actuator. Special focus was given in order to present a simple model for this servo actuator. The comparison between simulation and experimental results showed the effectiveness of the propose model. The model can be applied as a reference for characterizing different designs and future control strategies.
Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.
One of the common industrial structures that are used widely in many harbors and factories and buildings is overhead crane. Overhead cranes are usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane, a fuzzy controller designed with parallel distributed compensation and Linear Quadratic Regulation. With the Takagi-Sugeno fuzzy modeling, the nonlinear system is approximated by the combination of several linear subsystems in the corresponding fuzzy state space region. Then by constructing a linear quadratic regulation subcontroller according to each linear subsystem, a parallel distributed fuzzy LQR controller is designed. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. Simulation results illustrated the validity of the proposed control algorithm and it is compared with a similar method parallel distributed fuzzy controller.
In the petroleum industry perforating is a method of making holes through the casing opposite the production formation to allow the oil or gas to flow into the well. In the current explosive shaped charge perforation method there arc some serious problems, such as producing debris. uncontrollable hole size and shape, compaction of rock formation in the area next to the tunnel and decreasing permeability. Recent advances in high power laser technology provide a new alternative to replace the current perforating gun. Due to the nature of oil and gas reservoirs, one of the challenges in laser perforation is the laser beam-fluid interaction that results in laser power loss (LPL), In this paper, feed-forward network with back-propagation and generalized regression neural networks have been developed to predict LPL in the laser beam-fluid interaction during laser perforation. Effective parameters in the laser-fluid interaction such as laser power, fluid viscosity and fluid thickness which arc related to laboratory tests done by ytterbium-doped multi-clad fibre laser are the inputs and LPL is the output of the neural networks. The developed neural networks have shown high correlation coefficients with low error and the LPL for the laser beam-fluid interaction during laser perforation was predicted with high accuracy.
Relative gain array (RGA) is the most important and popular method of finding the best pairing in MIMO plants. However, selection of the pairs based on RGA is an offline algorithm with some ambiguity. In this article, the normalised RGA (NRGA) matrix is introduced through the combination of the RGA matrix and pairing rules. The pairing problem can be interpreted as an assignment problem by using NRGA. Therefore, Hungarian algorithm can be applied to pair inputs and outputs of the process. Up to this stage, integrity has not yet been considered. If the determined optimal pairing does not satisfy integrity conditions based on the Niederlinski index, the procedure continues through the suboptimal pairings. The algorithm is fully systematic. It is applied for control structure selection of the well known Tennessee Eastman process plant.
The main purpose of this paper is a study of the efficiency of different nonlinearity detection methods based on time-series data from a dynamic process as a part of system identification. A very useful concept in measuring the nonlinearity is the definition of a suitable index to measure any deviation from linearity. To analyze the properties of such an index, the observed time series is assumed to be the output of Volterra series driven by a Gaussian input. After reviewing these methods, some modifications and new indices are proposed, and a benchmark simulation study is made. Correlation analysis, harmonic analysis and higher order spectrum analysis are selected methods to be investigated in our simulations. Each method has been validated with its own advantages and disadvantages.
Overhead crane is an industrial structure that is widely used in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.
In this paper, we consider the problem of controlling chaos in scalar delayed chaotic systems. It is revealed that delayed feedback in the form proposed by Pyragas may cause delay in bifurcation. Also, it is shown that many choice of feedback gain and time delay make stable periodic solution for chaotic system which is fictitious. Finally, the period of these fictitious periodic orbits are estimated.
Nowadays computer games have become a billion dollar industry. One of the important factors in success of a game is its similarity to the real world. As a result, many AI approaches have been exploited to make game characters more believable and natural. One of these approaches which has received great attention is Fuzzy Logic. In this paper a Fuzzy Rule-Based System is employed in a fighting game to reach higher levels of realism. Furthermore, behavior of two fighter bots, one based on the proposed Fuzzy logic and the other one based on a scripted AI, have been compared. It is observed that the results of the proposed method have less behavioral repetition than the scripted AI, which boosts human players' enjoyment during the game.
In this paper, we introduce a vector which is able to describe the Niederlinski Index (NI), Relative Gain array (RGA), and the characteristic equation of the relative error matrix. The spectral radius and the structured singular value of the relative error matrix are investigated. The cases where the perfect result of the Relative Gain Array, equal to the identity matrix, coincides with the least interaction in a plant are pointed out. Then, the Jury Algorithm is adopted to get some insight into interaction analysis of multivariable plants. In particular, for interaction analysis of 3×3 plants, simple yet promising conditions in terms of the Relative Gain Array and the NiederLinski Index are derived. Several examples are also discussed to illustrate the main points.
In this paper the use of proportional-integralderivative (PID) switching controllers is proposed for the control of a magnetically actuated mass-spring-damper system which is composed of two masses M1 and M2; each mass is jointed to its own spring. Two different modes occur during the system motion; a PID controller is designed for each mode and a switching logic is applied in order to recognize the system's position to switch to the proper controller. Finally, simulation results are employed to show the performance of the proposed switched PID controller. Also, comparison results with the previously used model predictive controller (MPC) are provided.
Abstract: In this paper, the problem of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed for the control of highly uncertain plants. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a QFT controller-prefilter exists for robust stability and performance of the smaller uncertainy subsets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor compares the candidate local model behaviors with the one of the real plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. It is shown that this strategy improves closed loop performance, and can also handle the uncertainty sets that cannot be tackled by a single QFT robust controller. The multirealization technique to implement a family of controllers is employed to achieve bumpless transfer. Simulation results show the effectiveness of the proposed methodology.
Switching control is employed in many adaptive control strategies to overcome difficulties encountered in the control design problems that cannot be routinely solved by conventional robust and adaptive control architectures. A key stage in switching control design is the switching logic. This paper proposes a new switching scheme based on the control performance index (CPI) concepts. The performance assessment index is primarily calculated using the Markov parameters of the closed loop transfer function to assess the closed loop performance of the regulatory and tracking control systems. It is shown that employing CPI can lead to proper switching between different controllers. Finally, simulation results are provided show the main points of the paper.
This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical) is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.
Recently a lot of works have been done to detect faults in nonlinear systems. In this paper a new method, based on parity relations for linear systems, is proposed to detect faults in nonlinear systems that can be modeled by Takagi-Sugeno (TS) fuzzy system. This method is an intuitive generalization of parity relations, because TS fuzzy system uses local linear models. Results of simulation and implementation on a rotary inverted pendulum show that faults can be detected very well.
In this paper, fault tolerant synchronization of chaotic gyroscope systems via Gaussian RBF neural network based on sliding mode control is investigated. Taking a general nature of fault in the slave system into account, a new synchronization scheme, namely, fault-tolerant synchronization, is proposed, by which the synchronization can be achieved no matter if the fault and disturbance occur or not. By making use of a slave-observer and Gaussian RBF Neural Network Based on Sliding Mode Control, the fault tolerant synchronization can be achieved. The adaptation law of designed controller is obtained based on sliding mode control methodology without calculating the Jacobian of the system. The proposed method can compensate the actuator faults and disturbances occurred in the slave system. Numerical simulation results demonstrate the validity and feasibility of the proposed method to fault tolerant synchronization.
Breast cancer Dynamic magnetic resonance imaging (MRI) has emerged as a powerful diagnostic tool for breast cancer detection due to its high sensitivity and has established a role where findings from conventional mammography techniques are equivocal. In the clinical setting, the ANN has been widely applied in breast cancer diagnosis using a subjective impression of different features based on defined criteria. In this study, feature selection and classification methods based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It was collected from 2004 to 2006. A forward selection method is applied to find the best features for classification. Moreover, several neural networks classifiers like MLP, PNN, GRNN and RBF has been presented on a total of 112 histopathologically verified breast lesions to classify into benign and malignant groups. Also support vector machine have been considered as classifiers. Training and recalling classifiers are obtained with considering four-fold cross validation.
This paper aims to increase the classification specificity by using multi classifier system. First, a novel pixel search approach is applied to find significant region in images. Fuzzy C-means is utilized to determine the clear boundary of tumor. Then, shape and texture features are extracted from region of interest. Genetic algorithm is applied to select the best feature used for classifiers. Several neural networks and support vector machine are considered as classifiers that classify the data into benign and malignant group. To improve the performance of classification, three classifiers that have the best results among all applied methods are combined together that they have been named as multi classifier system. For each lesion, final detection as malignant or benign has been evaluated, when the same results are achieved from two classifiers of multi classifier system. Notice that the Jack-Knife technique is applied in this study, because it is useful for small data base as ours gotten from Milad Hospital in Tehran, Iran.
This paper presents a new hybrid control strategy for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. We employed a combination of LQR controller and fuzzy-neural network in a feedback error learning framework. In the proposed control approach, LQR controller as a classical controller is designed such that the stability is guaranteed and the control purposes are satisfied. Then an intelligent controller (FNN) which is working with the classical controller (LQR) takes the control task completely. It is shown that this technique (fuzzy-LQR) has good performance and also it has a very fast and proper response. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.
This paper proposes the generalized projective synchronization (GPS) of uncertain chaotic systems with external disturbance via Gaussian radial basis adaptive sliding mode control (GRBASMC). A sliding surface is adopted to ensure the stability of the error dynamics in sliding mode control. In the neural sliding mode controller, a Gaussian radial basis function is utilized to online estimate the system dynamic function. The adaptation law of the control system is derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for GPS of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Note that it needs only one controller to realize GPS no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. The proposed method is applied to three chaotic systems: Genesio system, Lur’e like system and Duffing system.
In this paper, a robust water level control system for the horizontal steam generator (SG) using the quantitative feedback theory (QFT) method is presented. To design a robust QFT controller for the nonlinear uncertain SG, control oriented linear models are identified. Then, the nonlinear system is modeled as an uncertain linear time invariant (LTI) system. The robust designed controller is applied to the nonlinear plant model. This nonlinear model is based on a locally linear neuro-fuzzy (LLNF) model. This model is trained using the locally linear model tree (LOLIMOT) algorithm. Finally, simulation results are employed to show the effectiveness of the designed QFT level controller. It is shown that it will ensure the entire designer’s water level closed loop specifications.
In this study, Extended Kalman Filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (HW) ARMAX models. The basic idea is to estimate the original parameters of the identification model, which are appeared in the form of product terms, directly. While, other algorithms like Extended Forgetting Factor Stochastic Gradient (EFG), Extended Stochastic Gradient (ESG), Forgetting Factor Recursive Least Square (FFRLS) and Kalman Filter (KF), estimate parameters in the product form and they need another algorithms such averaging method (AVE method), singular value decomposition method (SVD method) to separate the parameters. So, the computational complexity of the proposed approach decreases. To show the efficiency of this method the results are compared with EFG and ESG method.
In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input– output model is identified for the plant. To identify the various operation points in the kiln, locally linear neuro-fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 min prediction horizon. The other two models are presented for the two faulty situations in the kiln with 7 min prediction horizon. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used in this study.
This article considers an improvement in dead zone modification scheme for robust model-reference adaptive control of SISO and TITO systems, described by input-output uncertain linear models with actuator faults. In the conventional approach, adaptation of the controller parameters is ceased in the dead zone, which leads to steady state tracking error. This problem is resolved by tuning specific controller parameters inside the dead zone. The stability of the closed loop system and tracking of step commands are verified analytically. A comparative numerical simulation is performed to illustrate the effectiveness of the proposed scheme in control of an engine-dynamometer system.
In this paper, a new algorithm is presented in using Multi Layer Perceptron (MLP) and Radial Base Function (RBF) to predict Ischemia diseases by Electrocardiogram (ECG) signals. The process would be very difficult due to non-stationary and nonlinear characteristics of ECG signals. MLP and RBF algorithms are well known in predicting the problems. However, they have not been used for real time prediction through signals, especially bio signals such as ECG. Pre-processing is necessary for ECG signal in order to detect QRS complex. Regarding the extract influential features in Ischemia disease, the baseline wandering and noise suppression are done. MLP and RBF, the predictors, are employed to foresee the further next beats in ECG signals. The validity of predictor accuracy is evaluated by Root Mean Square Error (RMSE) criterion. After the prediction stage, The predicted beats are classified by Adaptive Neuro-Fuzzy network (ANFIS) classifier as ischemic and normal. MLP and RBF are tested for their abilities in order to predict Ischemic Heart Disease (IHD) upon ECG signals. The performances of classified beats are evaluated based on computed Sensitivity (Se) and Specificity (Sp). In this study several ECG signals recorded by European Society of Cardiology for ST-T database are used. By applying prediction methods (Direct and Recursive Predictions) 48 steps can be predicted ahead in ECG signal. Then the predicted beats are classified as Ischemic or normal beats. Therefore, the ischemic beats can be predicted in 48 steps ahead. By comparing the results obtained in this study, the MLP and RBF networks are evaluated for their capabilities in predicting Ischemia. According to this comparison, MLP shows better results and the results of ANFIS as a classifier has been satisfactory enough in classification of Ischemic beats. Therefore, these results can be used for early diagnosis of Ischemic Heart Disease (IHD).
This study proposed a model based fault detection and isolation (FDI) method using multi- layer perceptron (MLP) neural network. Detection and isolation of realistic faults of an industrial gas turbine engine in steady-state conditions is mainly centered. A bank of MLP models which are obtained by nonlinear dynamic system identification is used to generate the residuals, and also simple thresholding is used for the intend of fault detection while another MLP neural network is employed to isolate the faults. The proposed FDI method was tested on a single-shaft industrial gas turbine prototype and it have been evaluated using non-linear simulations based on the real gas turbine data. A brief comparative study with other related works in the literature on this gas turbine benchmark is also provided to show the benefits of proposed FDI method.
A 3-DOF image stabilizer (periscope) is modeled and controlled, such that target is fixed in the center of camera image. Its nonlinear dynamic equations are extracted by applying the Euler-Lagrange equation of rigid body motion in presence of friction. The extracted equations are second order motion equations. Moreover, fuzzy sliding mode controller (FSMC) for target tracking is applied to this nonlinear system where it is subject to uncertainty and external disturbances. The controller is MIMO and it has knowledge-based structure. This method eliminates chattering that exists in the conventional sliding mode. The effectiveness of the developed algorithm is validated by simulation results.
This paper presents a Neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a catalytic reformer unit in oil refinery plant. This unit include highly nonlinear behaviour and it is complicated to obtain an accurate physical model. There for, it is necessary to use such appropriate method providing suitable while preventing computational complexities. LOLIMOT algorithm as an incremental learning algorithm has been used several time as a well-known method for nonlinear system identification and estimation. For comparison, Multi Layer Perceptron (MLP) and Radial Bases Function (RBF) neural networks as well-known methods for nonlinear system identification and estimation are used to evaluate the performance of LOLIMOT. The results presented in this paper clearly demonstrate that the LOLIMOT is superior to other methods in identification of nonlinear system such as catalytic reformer unit (CRU).
Extreme nonlinearity and exhibition of severe interaction effects of multivariable pH processes makes it an appropriate test bed for evaluation of advanced controllers. This paper studies different multiple model methods for Generalized Predictive Control using Independent Model approach (GPCI) with adaptive weighting matrices. New method for adaptive determination of weighting matrices, proposed in this paper. Simulation results via typical multivariable pH process demonstrate the effectiveness and validity of the method. Different multiple model methods using adaptive weighting matrices compared with each other.
Neural networks are known as powerful tools to represent the essential properties of nonlinear processes because of their global approximation property. However, a key problem in modeling nonlinear processes by neural networks is the determination of neuron numbers. In this paper, a data based strategy for determining number of hidden layer neurons based on the Barrons work, describing function analysis and bicoherence nonlinearity measure is proposed. The proposed algorithm is evaluated for a pH neutralization process. It is shown that this algorithm has acceptable results.
In this paper, a Genetic-AIS (Artificial Immune System) algorithm is introduced for PID (Proportional-Integral-Derivative) controller tuning using a multi-objective optimization framework. This hybrid Genetic-AIS technique is faster and accurate compared to each individual Genetic or AIS approach. The auto-tuned PID algorithm is then fused in an Immune feedback law based on a nonlinear proportional gain to realize a new PID controller. Immune algorithm presents a promising scheme due to its interesting features such as diversity, distributed computation, adaptation and self monitoring. Accordingly, this leads to a more effective Immune-based tuning than the conventional PID tuning schemes benefiting a multi-objective optimization prospective. Integration of Genetic-AIS algorithm with Immune feedback mechanism results into a robust PID controller which is ultimately evaluated via simulation control test scenarios to demonstrate quick response, good robustness, and satisfactory overshoot and disturbance rejection characteristics.
In this paper, two Generalized Predictive Control algorithms (GPC, GIPC) are used to control X, Y axes of a Two-Degrees-of-Freedom robot, which is a earth station antenna related to be the HDF pedestals (High Dynamic Full Motion Leo Satellite Tracking Pedestals). This system model is achieved by using the Dymola software that according to the comparisons which have been done is very close to the actual system model and has very high accuracy. Comparing the simulation results between GPC and GIPC, fewer tracking errors are observed for the latter while it is much better when it comes to the disturbance rejection criterion.
In this paper control of the xy pedestal axes has been studied which is a two degrees of freedom earth station antenna of pedestal HDF family group (High Dynamic Full Motion Leo Satellite Tracking Pedestals). This system model is achieved by using the Dymola software that according to the comparisons which have been done is very close to the actual system model and has very high accuracy. Purpose: is to track a LEO orbit satellite that of passing satellites and angles related to the antenna have been extracted path from KNTUSAT software. In carried out simulation, the operation of PI controller has been optimized, MPC and GPC has been studied. The results of comparison between simulation methods show that predictive controller has had fewer errors in satellite tracking and has been shown less control effort and also has had good behavior in disturbance rejection.
This paper provides a Quantitative Feedback Theory (QFT) robust control design of a gas turbine in the presence of uncertain parameters. Frequency domain analysis, disturbance rejection properties for SISO and MIMO plants, are among the distinctive features of QFT. In this paper, a QFT robust controller satisfying the required performance despite uncertainties and various constraints on the control effort and process is designed. The nonlinear gas turbine simulator employed in this paper is based on the gas turbine thermodynamic characteristics presented within MATLAB-SIMULINK. The accuracy of this simulator has been examined through several tests by real gas turbine responses.
The pull-in instability places substantial restrictions on the performance of electrostatically driven MEMS devices by limiting their range of travel. Our objective is to present a systematic method of carrying out optimal design of novel types of electrostatic beams that have enhanced travel ranges. In this paper, we implement a shape optimization methodology using simulated annealing to maximize the static pull-in ranges of electrostatically actuated micro-cantilever beams. We use the Rayleigh-Ritz potential energy minimization technique to compute the pull-in displacement and voltage of each micro cantilever beam. A versatile parametric width function is used to characterize non-prismatic micro-cantilever geometries and the pull-in displacement of the cantilever is maximized with respect to the parameters of the proposed width function. Geometric constraints encountered in typical MEMS applications are incorporated into the optimization scheme using a penalty method. The simulated annealing algorithm uses different cooling schedules with the same number of objective function computations. We consider a matrix of several test cases in order to successfully demonstrate the utility of the proposed methodology. Our results indicate that an increase in the pull-in displacement of as much as 20% can be obtained by using our optimization approach. We have also compared our results with those obtained using traditional optimization approaches. We find the results are fairly independent of the cooling schedule used which demonstrates the usefulness and flexibility of this method to carry out optimal design of structural elements under electrostatic loading.
The aim of this study is to prove validity of feedback error learning rule for a linear representation of dynamic system with unknown parameters. A simple single-layer neural network is assumed as an adaptive linear combiner and stability techniques are applied to derive the same adaptation law as feedback error learning rule.
In this paper, the stabilization of linear time-invariant systems with fractional derivatives using a limited number of available state feedback gains, none of which is individually capable of system stabilization, is studied. In order to solve this problem in fractional order systems, the linear matrix inequality (LMI) approach has been used for fractional order systems. A shadow integer order system for each fractional order system is defined, which has a behavior similar to the fractional order system only from the stabilization point of view. This facilitates the use of Lyapunov function and convex analysis in systems with fractional order 1
In this paper, the stabilization of a particular class of multi-input linear systems of fractional order differential inclusions with state delay using variable structure control is considered. First, the sliding surface with a fractional order integral formula is defined, and then the sufficient conditions for stability of the sliding surface are derived. Also, the concepts related to sliding control stabilization of differential inclusion systems with integer order are extended to differential inclusion systems with fractional order 0
In this paper, a new approach for input-output pairing for stable and linear time invariant multivariable systems based on inputs-outputs correlation is introduced. Being independent from system's model is the characteristic of the proposed method. It is demonstrated that both static and dynamic properties of the system regarded in the proposed method. Through examples, the accuracy of the proposed approach is investigated. Finally, an example is used to show that in some cases Effective Relative Gain Array (ERGA) leads to improper pairs while the proposed method achieves the appropriate pairs.
This study addresses a nonlinear trajectory tracking control problem for a kinematics Model of nonholonomic mobile robot with considering next 2 time path curvature. The tracking control of mobile robot using two cascade controllers is presented. The first fuzzy controller produces a variable which shows curvature of the path and is considered as one of the inputs of the second fuzzy controller. Adaptive Neuro Fuzzy Inference System (ANFIS) is applied as second stage controller for the solution of the path tracking problem of mobile robots. The inputs value to fuzzy logic layer are VC, C, dR & dθ the robot current linear velocity, trajectory curvature, distance from the robot actual position to the next desired position, and difference between the angles of the dθ and the robot actual heading, respectively. A gradient descent learning algorithm is used to adjust the parameters. That present controller is compared with previous work to confirm its effectiveness.
In this study, a new type of training the adaptive network-based fuzzy inference system (ANFIS) is presented by applying different types of the Differential Evolution branches. The TSK-type consequent part is a linear model of exogenous inputs. The consequent part parameters are learned by a gradient descent algorithm. The antecedent fuzzy sets are learned by basic differential evolution (DE/rand/1/bin) and then with some modifications in it. This method is applied to identification of the nonlinear dynamic system, prediction of the chaotic signal under both noise-free and noisy conditions and simulation of the two-dimensional function. Instead of DE/rand/1/bin, this paper suggests the complex type (DE/current-to-best/1+1/bin & DE/rand/1/bin) on predicting of Mackey-glass time series and identification of a nonlinear dynamic system revealing the efficiency of proposed structure. Finally, the method is compared with pure ANFIS to show the efficiency of this method.
Dynamic Matrix Control (DMC) is well known in the MPC family and has been implemented in many industrial processes. In all the MPC methods, tuning of controller parameters is a key step in successful control system performance. An analytical tuning expression for DMC is derived using the analysis of variance (ANOVA) methodology and nonlinear regression. It is assumed that the plants under consideration can be modeled by a First Order plus Dead Time (FOPDT) linear model. This facilitates the derivation of a closed form formulae for the tuning procedure. The proposed method is tested via simulations and experimental work. The plant chosen for practical implementation of the proposed tuning strategy is a nonlinear laboratory scale pH plant. Also, comparison results are provided to show the effectiveness of this method.
This paper presents a variable structure control and anti control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory for variable structure control, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti-control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction, and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER's flexible-joint manipulator.
In this paper, an approach based on the variable structure control is proposed for stabilization of linear time invariant fractional order systems (LTI-FOS) using a finite number of available state feedback controls, none of which is capable of stabilizing the LTI-FOS by itself. First, a system with integer order derivatives is defined and its existence is proved, which has stability equivalent properties with respect to the fractional system. This makes it possible to use Lyapunov function and convex analysis in order to define the sliding sector and develop a variable structure control which enables the switching between available control gains and stabilizing the fractional order system.
The reliability of an intelligent self tuning controller called the brain emotional learning based intelligent controller (BELBIC) to attitude control of a nonlinear launch vehicle (LV) simulation with hardware-in-the loop simulation (HILS) is studied. To set up the HIL system of the LV a six-degree of freedom simulation of the LV and a hydraulic actuator, which was used for the pitch channel thrust vector control (TVC) actuator of the LV, is performed. The results of the BELBIC controller with a fuzzy controller (FC) and a PID controller in this HILS of the LV to control the pitch channel of the LV have been compared.
In this paper, a robust adaptive controller for accommodation of partial actuator faults is introduced. The proposed controller is based on the robust adaptive model-reference control scheme with improved dead zone modification. The common problem of steady state error in robust adaptive systems with simple dead zone modification is resolved by tuning a specific parameter inside the dead zone with a different adaptive law. Different types of actuator faults including output offset, loss of efficiency, and output delay are compensated with the proposed method. We behave these faults as uncertainties and disturbances. The proposed technique does not need an extra unit for fault detection and diagnosis. Comparative simulation studies are performed to illustrate the effectiveness of the proposed control technique versus the robust adaptive controller with simple dead zone modification.
When a detector sensitive to the target plume IR seeker is used for tracking airborne targets, the seeker tends to follow the target hot point which is a point farther away from the target exhaust and its fuselage. In order to increase the missile effectiveness, it is necessary to modify the guidance law by adding a lead bias command. The resulting guidance is known as target adaptive guidance (TAG). First, the pure proportional navigation guidance (PPNG) in 3-dimensional state is explained in a new point of view. The main idea is based on the distinction between angular rate vector and rotation vector conceptions. The current innovation is based on selection of line of sight (LOS) coordinates. A comparison between two available choices for LOS coordinates system is proposed. An improvement is made by adding two additional terms. First term includes a cross range compensator which is used to provide and enhance path observability, and obtain convergent estimates of state variables. The second term is new concept lead bias term, which has been calculated by assuming an equivalent acceleration along the target longitudinal axis. Simulation results indicate that the lead bias term properly provides terminal conditions for accurate target interception.
The use of intravenous drugs in general anesthesia is increasingly popular. Because of relatively precise injection rate, the goal of consistent control is possible. Because of using different drugs in full anesthesia for adequate hypnosis, analgesia and muscle relaxation, the interaction between drugs is more considerable especially when intravenous drugs are used. In this paper we use a developed Pharmacokinetic-Pharmacodynamic model which considers the interaction between two more popular intravenous drugs, Propofol for hypnosis and Remifentanil as an analgesic drug, to design a closed-loop system. The Radial Basis Function (RBF) controller as an adaptive neural controller was designed and adaptive properties of this structure in confront of variations in model parameters values was investigated. Trying to improve the tracking performance, one of most popular methods in hybrid control, Feedback Error Learning (FEL), was utilized.
Time delays are common in industrial processes. The information about the delay value of any process is valuable for both identification and control procedures. Several methods have been suggested for time delay estimation (TDE) in the literature. We propose a simple method based on plant input-output data. The concept of this data driven method is from combination of two well known approaches: Time delay estimation from impulse response and subspace identification. This method can be easily implemented in Multi Input-Multi Output (MIMO) plants. Also, by analyzing the window of input-output data in an online fashion, we can utilize our proposed method for time varying delay case. To verify the effectiveness of our proposed method, the developed procedures are applied to a pH plant model, a MIMO system and a time delay varying scenario. Simulation results demonstrate the effectiveness of the proposed method.
In this paper, logistic map is offered as a model for cardiac arrhythmia. In order to control cardiac chaos, a controller based on Delayed Feedback Control methodology is presented. This controller imposes the desired fixed-points on the map via an adaptive control law. Simulation results are provided to show the effectiveness of the proposed method. Finally advantages of the controller are mentioned.
In this study, adaptive control of flexible link model which is non-minimum phase and single- input, multiple-output (SIMO) is presented. The controllers designed aim to control the hub position in a way that attenuates the tip deflections with less energy consumption. Methods used to design the under actuated controller are WRBF network and neuro-fuzzy network and are compared to LQR and non-adaptive fuzzy controller. Learning method performed for adaptive schemes is emotional. Simulation results show the effectiveness of the designed controllers and reduction of energy demand in intelligent adaptive controllers.
Flexibility and aeroelastic behaviors in large space structures can lead to degradation of control system stability and performance. The model reference adaptive notch filter is an effective methodology used and implemented for reducing such effects. In this approach, designing a model reference for adaptive control algorithm in a flight device such as a launch vehicle is very important. In this way, the vibrations resulting from the structure flexibility mostly affects the pitch channel, and its influences on the yaw channel are negligible. This property is used and also the symmetrical behavior of the yaw and pitch channels. In this paper, by using this property and also the symmetrical behavior of the yaw and pitch channels, a new model reference using identification on the yaw channel is proposed. This model behaves very similar to the rigid body dynamic of the pitch channel and can be used as a model reference to control the vibrational effects. Simulation results illustrated applies the proposed algorithm and considerably reduces the vibrations in the pitch channel. Moreover, the main advantage of this new method is the online tuning of the model reference against unforeseen variations in the parameters of the rigid launch vehicle, which has not been considered in the previous works. Finally, robustness of the new control system in the presence of asymmetric behavior is investigated.
This paper presents a new tuning strategy for Generalized Predictive Controllers (GPC) based on Analysis of Variance (ANOVA). This strategy is derived for Second Order plus Dead Time (SOPDT) models of an industrial plant. Moreover, SOPDT modeling allows oscillating modes to be included in the model dynamics. The tuning strategy employs a simple expression for the tuning parameter as a function of plant parameters which is absent in earlier tuning attempts. This novel expression is extracted using ANOVA method combined with nonlinear regression. Also, a better performance index value and more convenient implementation are obtained in comparison with the conventional GPC tuning methods. Therefore, the tuning strategy for SOPDT models is both more comprehensive and more effective than traditional First Order plus Dead Time (FOPDT) model tunings. The proposed strategy is verified by two comparative simulation studies.
Dynamic Matrix Control (DMC) is a widely used model predictive controller (MPC) in industrial plants. The successful implementation of DMC in practical applications requires a proper tuning of the controller. The available tuning procedures are mainly based on experience and empirical results. This paper develops an analytical tool for DMC tuning. It is based on the application of Analysis of Variance (ANOVA) and nonlinear regression analysis for First Order plus Dead Time (FOPDT) process models. It leads to a simple formula which involves the model parameters. The proposed method is validated via simulations as well as experimental results. A nonlinear pH neutralization model is used for the simulation studied. It is further implemented on a laboratory scale control level plant. A robustness analysis is performed based on the simulation results. Finally, comparison results are provided to show the effectiveness of the proposed methodology.
This paper presents a new approach for breast cancer detection based on Hierarchical Fuzzy Neural Network (HFNN). Generally in formal fuzzy neural networks (FNN), increasing the number of inputs, causes exponential growth in the number of parameters of the FNN system. This phenomenon named as" curse of dimensionality". An approach to deal with this problem is to use the hierarchical fuzzy neural network. A HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. HFNN can use less rules to model nonlinear system. This method is applied to the Wisconsin Breast Cancer Database (WBCD) to classify breast cancer into two groups: benign and malignant lesions. The performance of HFNN is then compared with FNN by using the same breast cancer dataset.
Fuzzy modeling of high-dimensional systems is a challenging topic. This study proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. The proposed method works on the fuzzification layer and tries to use two-dimensional membership functions instead of onedimensional ones. This approach reduces fuzzy rule base radically due to using of two-dimensional membership functions which lead to reduction of parameters. The resulting fuzzy system generated by this method has the following distinct features: 1) the fuzzy system is quite simplified; 2) the fuzzy system is interpretable; 3) the dependencies between the inputs and the outputs are clearly shown. This method has successfully been applied to three classification problem and the results are compared with other works.
In this paper convergence speed of Least Mean Square (LMS) and Multi Stage Least Mean Square (MSLMS) in the active noise control systems have been studied and compared. The results show that MSLMS algorithm convergence rate is more efficient than LMS algorithm. Moreover; using of the above algorithms in the active noise control systems have been simulated. The simulation results show capability of the MLSM algorithm utilization in the active noise control systems.
Expanding mathematical models and forecasting the traffic flow is a crucial case in studying the dynamic behaviors of the traffic systems these days. Artificial Neural Networks (ANNs) are of the technologies presented recently that can be used in the intelligent transportation system field. In this paper, two different algorithms, the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) have been discussed. In the training of the ANNs, we use historic data. Then we use ANNs for forecasting a daily real time short-term traffic flow. The ANNs are trained by the Back-Propagation (BP) algorithm. The variable coefficients produced by temporal signals improve the performance of the BP algorithm. The temporal signals provide a new method of learning called Temporal Difference Back-Propagation (TDBP) learning. We demonstrate the capability and the performance of the TDBP learning method with the simulation results. The data of the two lane street I-494 in Minnesota city are used for this analysis.
In this paper, the problem of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed for the control of highly uncertain plants. According to this strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a QFT controller exits for robust stability and performance of the individual uncertain sets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. It is shown that this strategy leads to improved closed loop performance, and can also handle the uncertainty sets that can not be tackled by a single QFT robust controller. Simulation results are proposed to show the effectiveness of the proposed methodology.
This paper describes hybrid multivariate method: Principal Component Analysis improved by Genetic Algorithm. This method determines main Principal Components can be used to detect fault during the operation of industrial process by neural classifier. This technique is applied to simulated data collected from the Tennessee Eastman chemical plant simulator which was designed to simulate a wide variety of faults occurring in a chemical plant based on a facility at Eastman chemical.
In this paper, a new feature selection and classification methods based on artificial neural network are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It is collected from 2004 to 2006.
This paper investigates the use of anL1adaptive controller direct approach to solve the attitude control problem of a launch vehicle (LV) during its atmospheric phase of flight. One of the most important difficulties in designing a controller for launch vehicles (LVs) is the widely changing system parameters during launch. Aerospace systems such as aircraft or missiles are subject to environmental and dynamical uncertainties. These uncertainties can alter the performance and stability of these systems. Unknown variations in thrust and atmospheric properties, eccentricities of nozzles, and other unknown conditions cause changes in a system. The L1 adaptive controller ensures uniformly bound transient and asymptotic tracking for the system’s signals – input and output – simultaneously. This adaptive control technique quickly compensates for large changes in the LV dynamics. The effect of feedback gain selection and robustness of this approach against system uncertainties and actuator disturbances are also discussed. The adaptive control method is then simulated with representative LV longitudinal motion. The effectiveness of the proposed control schemes is demonstrated through hardware-in-the- loop simulation.
n this paper, an extension of the modified generalized predictive control (GPC) algorithm and a tuning strategy is presented. To take the plant dynamics such as under damped behavior and the effect of zeros into account, extension to the second order plus dead time (SOPDT) of the first order plus dead time (FOPDT) modified GPC method is proposed. It is shown that this method is computationally undemanding. Also, implementation is more straightforward than conventional GPC algorithms. Moreover, the proposed tuning strategy enables a fast implementation of the GPC with regard to nominal stability and desired performance. The simplicity of this strategy and its wide applicability makes it readily accessible to practitioners for utilization. Multiple simulation results are provided to show the effectiveness of the proposed algorithm.
A comprehensive gas turbine fault diagnosis system has been designed using a full nonlinear simulator developed in Turbotec company for the V94.2 industrial gas turbine manufactured by Siemens AG. The methods used for detection and isolation of faulty components are gas path analysis (GPA) and extended Kalman filter (EKF). In this paper, the main health parameter degradations namely efficiency and flow capacity of the compressor and turbine sections are estimated and the responsible physical faults such as fouling and erosion are found. Two approaches are tested: The single-operating point and the multi-operating point. Simulation results show good estimations for diagnosis of most of the important degradations in the compressor and turbine sections for the single-point and improved estimations for the multi-point approach.
In this paper, the relation between Input-output pairing and minimum variance (MV) index as a performance index is studied. Control structure selection or the input-output pairing problem is a key step in designing decentralized controllers for multivariable. The Relative Gain Array (RGA) is an important tool for the control structure selection procedure. In this study, RGA is calculated and decentralized minimum variance controllers are designed for each feasible pairing. The MV performance index will be calculated from the closed loop transfer function using the markov parameters. It is shown that the value of the MV index can propose an input-output pairing that leads to minimum output variance. Several simulation results are provided to show the main points of the paper.
Abstract The multiple modeling and controlapproach is a proper method for modeling and controlof nonlinear systems which their dynamics changesrapidly at different operating conditions. The dynamicof the twin rotor helicopter in vertical direction isnonlinear and changes instantaneously with respect tochange of the elevation angle, and it can be used forimplementation of multiple modeling and controlapproach. In this paper the performance of multiplemodeling and control approach by simulation andimplementation has been investigated.
According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy mean and root mean square (RMS), energy entropy mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy mean and root mean square is proposed. The analysis results from energy entropy and root mean square of different vibration signals show that the energy and root mean square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy mean and root mean square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and root mean square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.
Gas turbines are used widely in power generation, oil and gas industries, process plants and aviation. Efficiency and reliability is crucial in such applications. Hence, accurate modeling and control system designing is necessary. This paper first presents a nonlinear modeling of a single-shaft gas turbine in power generation application. This model is developed by solving differential and algebraic thermo dynamic equations and using turbine's component maps. Using this complex model, a number of linear models is identified around turbine's operating points. Effect of frequency and ambient condition is also considered in the models. Comparing these models, reduced number of linear models is selected to cover turbine's entire operating range. These models are validated using further identification tests and nonlinear model responses.
Multi-objective design problem is the optimization of various and often conflicting objectives for a complex system. In this paper, optimization is performed using Linear Matrix Inequalities (LMI's). A switching strategy is proposed in order to improve the multi-objective control performance. Each controller design is based on a set of performance specifications. Instead of considering all the specifications defined by respective LMI sets simultaneously, only relevant objectives are included in the control design procedure. Then, switching is performed to meet multiple objectives. Assurance of the overall stability of the closed-loop is acknowledged via specific controller realization. Multi-objective designs are prone to conservatism, which is greatly reduced by the switching approach. The efficiency of the proposed methodology is illustrated through an example.
A nonlinear model predictive control (NMPC) algorithm based on a neural network model is proposed for multivariable nonlinear systems. A multi-input multi-output model is developed using multilayer perceptron (MLP) neural network which is trained by Levenberg-Marquardt algorithm and amplitude modulated pseudo random binary (APRBS) signals with noise as data sets. Model predictive control also uses Levenberg-Marquardt algorithm for the control signal optimization. The control performance is improved by using a disturbance model that compensates both model mismatch and external disturbance. The learning rate of disturbance estimation network changes adaptively to treat the model mismatch differently from the external disturbance. Simulation results using the quadruple-tank are employed to show the effectiveness of the method.
This paper proposes a neural sliding mode control scheme for the synchronization of two chaotic nonlinear gyros subject to uncertainties and external disturbance. In this scheme, sliding mode control and multi layer perceptron neural network control are combined. A sliding surface is adopted to ensure the stability of the error dynamics in sliding mode control. The adaptation law of the multi layer perceptron neural network control system is derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. By Lyapunov stability theory, neural sliding mode control is presented to ensure the stability of the controlled system. Multi layer perceptron Neural Network control is trained during the control process. The proposed method allows us to synchronize gyros by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigenvalues of the jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for chaos synchronization of uncertain nonlinear gyro systems. Note that it needs only one controller to realize synchronization and the controller is easy to be implemented. The simulation results demonstrate the ability of the neural sliding control scheme to synchronize the chaotic gyro systems.
A number of techniques for detection of faults in ball bearing using frequency domain approach exist today. For analyzing non-stationary signals arising out of defective rolling element bearings, use of conventional discrete Fourier Transform (DFT) has been known to be less efficient. One of the most suited time-frequency approach; wavelet transform (WT) has inherent problems of large computational time and fixed-scale frequency resolution. In view of such constraints, the Hilbert-Huang Transform (HHT), technique provides multi-resolution in various frequency scales and takes the signal's frequency content and their variation into consideration. HHT analyses the vibration signal using intrinsic mode functions (IMFs), which are extracted using the process of empirical mode decomposition (EMD). HHT is effective in many different fields but lacks proper theoretical support. The time resolution significantly affects the calculation of corresponding frequency content of the signal. In this paper Firstly, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary. Secondly, we choose some special IMFs to obtain Hilbert transform and then Hilbert marginal spectrum and the last local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Finally, the characteristic amplitude ratios serve as the fault characteristic vectors to be input to the multi-class support vector machine (MSVM) classifiers and the work condition and fault patterns of the roller bearings and then faults are diagnosis real time based on Voting.
In this research, a semi active control system with continuous variations along with hydro active dampers and springs is developed for a passenger car. The improvement of dynamic behavior of a passenger car with regard to weight constraint, energy consumption and cost highlights the need for the employment of such a semi active suspension system. Here, a full car model with hydro active subsystems including roll, pitch, bounce movements, and one degree of freedom for the driver is extracted, unlike the previous research in which merely the bouncing motion has been taken into account. By using the linearized car model equipped with the proposed hydro active system, the optimal damping force based on full state feedback control and LQR method is obtained for the improvement of the ride comfort and stability. In addition, practical constraints on manufacturing of the components and delay in the control system are dealt with. The car is excited by a disturbance such as a bump imparted to the wheels for which the idea of the wheel based filtering was also considered. The simulation results in the time domain for the hydro active suspension system demonstrate significant improvements in all controlled modes in comparison with the passive system. On the other hand, in comparison with the fully active system, the proposed system has an additional advantage in terms of energy consumption and weight reduction in the required hardware.
One of the most common cardiovascular diseases is Myocardial Ischemia (MI). The aim of this study is improving the diagnosis level of Ischemic Beat detection from ECG signals which is important in ischemic episode detection process. This improvement is based on appropriate feature extraction via Multi resolution Wavelet analysis and proper classifier selection. The approach starts with signal preprocessing, Afterwards efficacious morphologic features which are useful in ischemia detection are extracted by wavelet analysis. In the third stage subtractive clustering is performed for clustering. Finally probabilistic neural networks are used as a classifier. The proposed algorithm is evaluated on the European Society of Cardiology (ESC) ST-T database and reported 96.67% sensitivity and 89.18% specificity.
Inherent pH process nonlinearity and time-varying characteristics impose a highly challenging control problem. This paper presents an incorporation of offline process model identification and a QFT control methodology to develop a robust control scheme for a pH neutralization process plant on the basis of SISO QFT bounds. The obtained simulation results indicate the efficiency of the proposed control scheme to accomplish both the regulatory and servo tracking objectives
To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of traffic systems. METANET model is one of the most applicable models in traffic modeling which parameters have plenty of effects on model behavior. In this paper, we describe the effects of the model parameters on the model behavior and the estimation quality of system states in the case of undetermined parameters. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic traffic networks for preparing proper signal in traffic control.
Developing mathematical models and estimating their parameters are fundamental issues for studying dynamic behaviors of traffic systems. METANET model is one of the most applicable models in traffic modeling in which the parameters have plenty of effects on the model behavior. In this paper, the effects of the model parameters on the model behavior and the estimation quality of the system states in the undetermined parameters are described. The extended Kalman filtering (EKF) algorithm instead of the error back-propagation (BP) algorithm is used to train artificial neural networks (ANNs) for dynamical traffic networks modeling. The basic idea is to prevent over fitting discrepancy occurrence caused by outliers in the training samples by the EKF. Numerical simulations show that the EKF algorithm is greater to the BP algorithm.
Objective: The implementation of family psychoeducation at the service delivery level is not without difficulty. Few mental health professionals receive special training to work with families especially in Iran. The aim of the present study was to evaluate the effectiveness of training health professionals in terms of their adherence to protocol. Method: Eight professionals (general practitioners, nurses and social workers) participated in a training program for health professionals as part of the Roozbeh First-Episode Psychosis Program (RooF) to conduct family psychoeducation. Training included a 3-day- workshop and 12 supervision sessions during the course of the implementation of the psychoeducation program. The family psychoeducation sessions (multiple-family group or single-family home-based) were tape-recorded. Transcripts of the audiotaped sessions were analyzed based on the content of the manual and were scored accordingly. Results: Twenty-four recorded sessions were analyzed in terms of the adherence to protocol, the number of questions and the time for each session. The overall rating showed a 72% adherence to the protocol. Multiple-family group sessions had a higher rate compared to the single-family home-based family psychoeducation sessions (79% to 69%) as well as the time spent and questions asked. The rate of adherence to the protocol of conducting the family psychoeducation sessions had not changed over time. Conclusion: Considering the amount of time taken for training and supervision, the level of adherence to the protocol was satisfactory. Tape recording sessions and regular supervision would be beneficial following specialized training. Further research is needed to tailor the amount of training and supervision required for professionals to conduct family psychoeducation programs in different settings.
This paper disputes what Blanchard and Kahn have reported as the solution of linear rational expectation (RE) systems many years ago. Their method leads to traditional determinacy condition which is used very much nowadays. In this paper we have a new look to the mathematical procedure of this solution method and the main problem in their solution will be shown. We introduce a new methodology for modeling the systems with expectation, while in future this way of modeling can be used to replace traditional RE models.
In this paper we present a systematic procedure to design robust fuzzy controller for exponentially stabilizing affine nonlinear systems, based on their TS fuzzy model. For robust design we consider modeling error in TS model and as well as perturbation in the original nonlinear system. Minimization of cost function along with mapping closed loop poles to desired poles are considered simultaneously in controller design. As a result, the desired specified performance in transient response can be achieved. Piecewise Discontinues Lyapunov Functions (PDLF) are utilized in our proposed method. To avoid difficulties in boundary conditions in PDLF we opt to design an online controller and check the regions and boundaries continuously. The constraints required to guarantee the exponential stability of the original nonlinear systems and optimal controller design with guaranteeing desired performance are presented in the LMI form. The y well developed. The power of these methods is that searching Lyapunov function and feedback gain can be stated as a convex optimization problem and the task of finding the common Lyapunov function can be readily be formulated into an LMI problem. However this approach is too conservative and there are lots of stable systems that we can not find a common positive definite Lyapunov function for all subsystems. Piecewise quadratic Lyapunov function approach , have been considered to avoid conservativeness of quadratic Lyapunov function approaches -. Piecewise quadratic Lyapunov function (PLF) are divided in two categories, one is continuous (PCLF) in boundaries and one of them is discontinuous (PDLF) on boundaries. It was shown that PDLF in contrast with PCLF results in fewer LMIs . To apply all mentioned methods, the system must be presented by a Takagi-Sugeno model and as it was demonstrated TS modeling enables us to deal with high order complicated nonlinear systems. Most of works so far have used PCLF for controller design and stability analysis, but PDLF have been used mainly for stability analysis and there are no reports about using PDLF for controller design. The main reason is difficulties in boundary conditions. effectiveness and applicability of the proposed method is examined on an inverted pendulum system.
In this paper, we use system identification methods for abnormal condition detection in a cement rotary kiln. After selecting proper inputs and output, an input–output model is identified for the plant’s normal conditions. A novel approach is used in order to estimate the delays of the input channels of the kiln before identification part. This method eases the identification since with determining the input channels delays, the dimension of search space in the identification part reduces. Afterward, to identify the kiln’s model, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOcally LInear MOdel Tree (LOLIMOT) algorithm which is an incremental tree-structure algorithm. Finally, with the model for normal condition of the kiln, the incident of abnormalities in output are detected based on the length of duration and magnitude of difference between the real output and model’s output. We distinguished three abnormal conditions in the kiln, two of which are known as common abnormal conditions and another one which was not characteristically known for cement experts either.
This paper proposes a new method for the adaptive control of nonlinear in parameters (NLP) chaotic systems. A method based on Lagrangian of a cost function is used to identify the parameters of the system. Estimation results are used to calculate the Lyapunov exponents adaptively. Finally, the Lyapunov exponents placement method is used to assign the desired Lyapunov exponents of the closed loop system.
This paper presents an adaptive nonlinear control scheme aimed at the improvement of the handling properties of vehicles. The control inputs for steering intervention are the steering angle and wheel torque for each wheel, i.e., two control inputs for each wheel. The control laws are obtained from a nonlinear 7-degree-of-freedom (DOF) vehicle model. A main loop and eight cascade loops are the basic components of the integrated control system. In the main loop, tire friction forces are manipulated with the aim of canceling the nonlinearities in a way that the error dynamics of the feedback linearized system has sufficient degrees of exponential stability; meanwhile, the saturation limits of tires and the bandwidth of the actuators in the inner loops are taken into account. A modified inverse tire model is constructed to transform the desired tire friction forces to the desired wheel slip and sideslip angle. In the next step, these desired values, which are considered as setpoints, are tackled through the use of the inner loops with guaranteed tracking performance. The vehicle mass and mass moment of inertia, as unknown parameters, are estimated through parameter adaptation laws. The stability and error convergence of the integrated control system in the presence of the uncertain parameters, which is a very essential feature for the active safety means, is guaranteed by utilizing a Lyapunov function. Computer simulations, using a nonlinear 14-DOF vehicle model, are provided to demonstrate the desired tracking performance of the proposed control approach.
It is widely accepted in the brain computer interface research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Independent component analysis is a method that blindly separates mixtures of independent source signals, forcing the components to be independent. It has been widely applied to remove artifacts from EEG signals. Preliminary studies have shown that ICA increases the strength of motor-related signal components in the Mu rhythms, and is thus useful for removing artifacts in BCI systems.
Monitoring and controlling the depth of anesthesia in surgery is so important. Compartmental models are well suited for closed-loop control of drug administration. In this paper, we develop a neural network and a fuzzy controller for nonlinear and compartmental system with nonnegative control input. In addition, the controllers guarantee that the physical system states remain in the nonnegative state space. After that, the proposed approaches are used to control the infusion of the anesthetic drug propofol in order to maintain a desired constant level of the depth of anesthesia for noncardiac surgery. In the end, this goal can be reached that intelligent systems are better than classic adaptive controller in adjustment of anesthesia with suitable condition of patient.
This paper investigates chaos control for scalar delayed chaotic systems using sliding mode control strategy. Sliding surface design is based on delayed feedback controller. It is shown that the proposed controller can achieve stability for an arbitrary unstable fixed point (UPF) or unstable periodic orbit (UPO) with arbitrary period. The chaotic system used in this study to illustrate the theoretical concepts is the well known Mackey–Glass model. Simulation results show the effectiveness of the designed nonlinear sliding mode controller.
This Letter deals with the problem of designing time-delayed feedback controllers (TDFCs) to stabilize unstable equilibrium points and periodic orbits for a class of continuous time-delayed chaotic systems. Harmonic balance approach is used to select the appropriate controller parameters: delay time and feedback gain. The established theoretical results are illustrated via a case study of the well-known Logistic model.
This paper proposes a hybrid control scheme for the synchronization of two chaotic nonlinear gyros, subject to uncertainties and external disturbances. In this scheme, Linear Quadratic Regulation (LQR) control, Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control are combined. By Lyapunov stability theory, SM control is presented to ensure the stability of the controlled system. GRBFNN control is trained during the control process. The learning algorithm of the GRBFNN is based on the minimization of a cost function which considers the sliding surface and control effort. Simulation results demonstrate the ability of the hybrid control scheme to synchronize the chaotic gyro systems through the application of a single control signal.
The alcoholism is one of psychiatric phenotype, which results from interplay between genetic and environmental factors. Not only it leads to brain defects but also associated cognitive, emotional, and behavioral impairments. It can be detected by analyzing EEG signals. In this research, the power spectrum of the Haar mother wavelet is extracted as features. Then the principle component analysis is applied for dimension reduction of the feature vectors. Finally support vectors machine and neural networks are used for classification. The simulation results show that our proposed method achieves better classification accuracy than the other methods.
Rotary cement kiln is the main part of a cement plant that clinker is produced in it. Clinker is the main ingredient of cement. Continual and prolonged operation of rotary cement kiln is vital in cement factories. However, prolonged operation of the kiln is not possible and periodic repairs of the refractory lining would become necessary, due to non-linear phenomena existing in the kiln, such as sudden falls of coatings in the burning zone and probability of damages to the refractory materials during production. This is the basic reason behind the needs for a comprehensive model which is severely necessary for better control of this process. Such a model can be derived based on the mathematic analysis with consultation of expert operator experiences. In this paper both linear and nonlinear model are identified for rotary kiln of Saveh white cement factory. The linear model is introduced using Box-Jenkins structure. The results of the obtained model were satisfactory compared to some other linear models and can be used for designing adaptive or robust controllers. Also, nonlinear system identification via Neural Network technique is performed and its result was compared to linear models.
This paper studies Internal Model Control (IMC) and its structure and applications in process. By using he capability of IMC we obtained PID coefficients and designed the IMC-PID controller. Then the IMC-PID is used in multi controller structure to control the pressure plant (RT532).
In this paper, design of decentralized switching control for uncertain multivariable plants based on the Quantitative Feedback Theory (QFT) is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input-output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Simulation results are employed to show the effectiveness of the proposed method.
A multiple-model adaptive controller is developed using the Self-Organizing Map (SOM) neural network. The considered controller which we name it as Multiple Controller via SOM (MCSOM) is evaluated on the pH neutralization plant. In MCSOM multiple models are identified using an SOM to cluster the model space. An improved switching algorithm based on excitation level of plant has also been suggested for systems with noisy environments. Identification of pH plant using SOM is discussed and performance of the multiple-model controller is compared to the Self Tuning Regulator (STR).
Control system design for a complex system encompasses the optimization of different and often conflicting objectives leading to a multiple objective design problem. In this paper, a switching strategy is proposed to solve the multiple objective controller design. Each controller is designed based on a set of performance specifications. Control realization considerations are used to ensure overall closed loop stability. Linear matrix inequalities are employed in the controller design process. Multi objective designs are always prone to over conservatism, which is greatly reduced by the switching strategy. Simulation results are used to show the effectiveness of the design methodology.
Past work on face detection has emphasized the issues of feature extraction and classification, however, less attention has been given on the critical issue of feature selection. We consider the problem of face and non-face classification from frontal facial images using feature selection and neural networks. We argue that feature selection is an important issue in face and non-face classification. Automatic feature subset selection distinguishes the proposed method from previous face classification approaches. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space, spanned by the eigenvectors of the covariance matrix of the training images (i.e., coefficients of the linear expansion).Then we consider Linear Discrimination Analysis (LDA) to achieve a comparison result between these two methods of dimension reduction. Genetic Algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain eigenvectors that do not seem to encode important information about face. Finally, a Probabilistic Neural Network (PNN) is trained to perform face classification using the selected eigen-feature subset. Experimental results demonstrate a significant improvement in error rate reduction.
Brain Computer Interface one of hopeful interface technologies between humans and machines. Electroencephalogram-based Brain Computer Interfaces have become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced in electroencephalography data by muscle activity, eye blinks and electrical noise is a common and important problem in electroencephalography research. In this research, we used five different methods for detecting trials containing artifacts. Finally we used two different neural networks, and support vector machine to classify features that are extracted by wavelet transform.
Double Inverted Pendulum is a nonlinear system, unstable and fast reaction system. Double Inverted Pendulum is stable when its two Pendulums allocated in vertically position and have no oscillation and movement and also insertion force should be zero. The main target of this research is design a controller based on Neuro-Fuzzy methods by using feedback- error-learning for controlling double inverted Pendulum.
This study proposes a Gaussian Radial Basis Adaptive Backstepping Control (GRBABC) system for a class of n-order nonlinear systems. In the neural backstepping controller, a Gaussian radial basis function is utilized to on-line estimate of the system dynamic function. The adaptation laws of the control system are derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. The proposed GRBABC is applied to two nonlinear chaotic systems which have the different order to illustrate its effectiveness. Simulation results verify that the proposed GRBABC can achieve favorable tracking performance by incorporating of GRBFNN identification, adaptive backstepping control techniques.
This paper proposes the generalized projective synchronization for chaotic systems via Gaussian Radial Basis Adaptive Backstepping Control. In the neural backstepping controller, a Gaussian radial basis function is utilized to on-line estimate the system dynamic function. The adaptation laws of the control system are derived in the sense of Lyapunov function, thus the system can be guaranteed to be asymptotically stable. The proposed method allows us to arbitrarily adjust the desired scaling by controlling the slave system. It is not necessary to calculate the Lyapunov exponents and the eigen values of the Jacobian matrix, which makes it simple and convenient. Also, it is a systematic procedure for generalized projective synchronization of chaotic systems and it can be applied to a variety of chaotic systems no matter whether it contains external excitation or not. Note that it needs only one controller to realize generalized projective synchronization no matter how much dimensions the chaotic system contains and the controller is easy to be implemented. The proposed method is applied to three chaotic systems: Genesio system, Rössler system, and Duffing system.
This study describes hybrid control methods to control a flexible manipulator with payload. The dynamic equation of the system has been derived by Lagrange`s method. The designed controllers consist of two parts, classical controllers, PID and Linear Quadratic Regulation (LQR) and hybrid controllers, Fuzzy Neural Network (FNN) controller with Feedback Error Learning (FEL) and Sliding mode control using Gaussian Radial Basis Function Neural Network (RBFNN). The fuzzy neural network and radial basis function neural network are trained during control process and they are not necessarily trained off-line.
The CE 150 made by Humusoft is a laboratory helicopter designed for studying system dynamics and control engineering principles. This helicopter is nonlinear and unstable in vertical direction. In this paper a linear bank of models for modeling a vertical direction of helicopter has been obtained, and model validation is discussed.
One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear identification technique on the Locally Linear Neuro-Fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise fifteen minute prediction horizon for a cement rotary kiln is presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these models. The data collected from White Saveh Cement Company is used for modeling.
This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and gradient descent (GD) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. This paper, studies the stability of PSO as an optimizer in training the identifier, for the first time. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data.
This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network based Fuzzy Inference System (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on particle swarm optimization (PSO) for training the antecedent part and forgetting factor recursive least square (FFRLS) for training the conclusion part. Two famous training algorithms for ANFIS are the gradient descent (GD) to update antecedent part parameters and using GD or recursive least square (RLS) to update conclusion part parameters. Lyapunov stability theory is used to study the stability of the proposed algorithms. This paper, also studies the stability of PSO as an optimizer in training the identifier. Stable learning algorithms for the antecedent and consequent parts of fuzzy rules are proposed. Some constraints are obtained and simulation results are given to validate the results. It is shown that instability will not occur for the leaning rate and PSO factors in the presence of constraints. The learning rate can be calculated on-line and will provide an adaptive learning rate for the ANFIS structure. This new learning scheme employs adaptive learning rate that is determined by input–output data. Also, stable learning algorithms for two common methods are proposed based on Lyapunov stability theory and some constraints are obtained.
In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.
This paper provides an extended pairing criterion based on the effective relative gain array. The extension is achieved in two steps. First, an energy based compromise between steady state gain and bandwidth information of the plant is proposed. Then, it is argued that the best pairing may depend on the closed-loop specifications. Thus, to make this extension practical and precise, a simple solution to take into account the bandwidth of the desired closed-loop plant is introduced. To show the effectiveness of the proposed method, several examples are discussed. These examples include the cases where the conventional ERGA leads to an appropriate result and is in agreement with the proposed pairing criterion. They also include the cases where the original ERGA leads to an improper pairing while the proposed method achieves the acceptable pairs.
Rotary cement kiln is the main part of a cement plant that clinker is produced in it. Continual and prolonged operation of rotary cement kiln is vital in cement factories. However, continual operation of the kiln is not possible and periodic repairs of the refractory lining would become necessary, due to non-linear phenomena existing in the kiln, such as sudden falls of coatings in the burning zone and probability of damages to the refractory materials during production. This is the basic reasoning behind the needs for a comprehensive model which is severely necessary for better control of this process. Such a model can be derived based on the mathematical analysis with consultation of expert operator experiences. In this paper linear model is identified for rotary kiln of Saveh white cement factory. The linear model is introduced using Box-Jenkins structure. The results of the obtained model were satisfactory compared to some other models and can be used for designing adaptive or robust controllers.
Clay-rich sediments from South Abarkouh district of clay deposit (SADC) in central Iran were analyzed for mineralogical and chemical composition, including the Rare earth element contents. Fifteen clay deposits have been located in Lower Permian (Artinskian) sediments of the area. The sediments are dominated by kaolinite, illite and quartz and minor phases include chlorite, albite, goethite, paragonite, natroalonite and gypsum. Whole rock chemistry shows that sediment samples rich in SiO2 and Al have low Fe, Sc and Cr contents. The high Chemical Index of Alteration (CIA) values, high Chemical Index of Weathering (CIW) values, high ratio of TiO2/Zr and low contents of the alkali and alkali earth elements of the clay-rich sediments suggest a relatively more intense weathering source area. Barium, Rb, Ca and Mg were probably flushed out by water during sedimentation. The chondrite-normalized Rare earth element patterns of the clay-rich sediments show LREE enrichments and a negative Eu anomaly. The high chondrite normalized La/Yb ratios and Gd/Yb ratios lower than 1.3, indicate that the sediments are enriched in LREEs. The mineralogical composition, REE contents, main elements discrimination diagram and elemental ratios in these sediments such as TiO2/Al2O3 suggest a provenance mainly felsic rocks, with only minor contributions from basic sources. The basic sediments were most likely derived from Granitic-Riolitic rocks. The most significant geochemical finding is that despite intense weathering, which has affected most elements, the REE, Th and Sc remain immobile. The chemistry and the mineralogy of the studied samples, compared to other commercial clays, shows that they need some treatment to render them suitable for ceramics production.
Multi-objective control is the problem of optimising various conflicting objectives. In this paper, the multi-objective active vibration control via switching is proposed. The switching is applied to the separately designed H 2 and H w controllers, instead of considering both objectives in the synthesis of a single controller. Each controller is designed using Linear Matrix Inequalities (LMIs). The overall stability of the closed-loop is guaranteed through a specific controller realisation. The H 2 controller is utilised to improve the transient response and the H w controller in steady-state performance. The switching approach in multi-objective control reduces the conservatism of the design. Control of the active vibration system as a regulator is studied in the present paper.
This paper suggests a novel approach for control of a flexible-link based on the feedback- error-learning (FEL) strategy. A radial basis function neural network (RBFNN) is used as an adaptive controller to improve the performance of a lead compensator controller in FEL structure. This scheme is developed by using a modified version of the FEL approach to learn the inverse dynamic of the flexible manipulator which requires only a linear model of the system for designing lead compensators and RBFNN controllers. The final controller should allow the user to command a desired tip angle position. The controller should eliminate the link's vibrations while maintaining a desirable level of response. Finally, the control performance of the proposed control approach for tip position tracking of flexible-link manipulator is illustrated by simulation result.
A new approach to adaptive control of chaos in a class of nonlinear discrete-time-varying systems, using a delayed state feedback scheme, is presented. It is discussed that such systems can show chaotic behavior as their parameters change. A strategy is employed for on-line calculation of the Lyapunov exponents that will be used within an adaptive scheme that decides on the control effort to suppress the chaotic behavior once detected. The scheme is further augmented with a nonlinear observer for estimation of the states that are required by the controller but are hard to measure. Simulation results for chaotic control problem of Jin map are provided to show the effectiveness of the proposed scheme.
Objectives: The aim of this study was to evaluate the outcome of a group of patients with bipolar I disorder admitted to Roozbeh Hospital, Tehran, Iran, during one year follow up. Method: In this prospective naturalistic study, 131 subjects with bipolar I disorder who were consecutively admitted to the hospital were enrolled. Patients were assessed at baseline, discharge, and 6 and 12 months after admission to the hospital. Different aspects of response to treatment including severity of mood and psychotic symptoms, extrapyramidal side effects, global functioning and service satisfaction were assessed using a demographic questionnaire, Young Mania Rating Scale (Y-MRS), and Hamilton Rating Scale for Depression (HAM-D). Results: Severity of symptoms and function showed significant improvement only at discharge (p<0.001), and was not significant afterwards. Patients showed a response rate of 65.4% based on 50% decrease on (Y-MRS), at discharge. Conclusion: Improvement in symptom severity and global functioning was significant at discharge but there was no significant improvement after discharge and during one year follow up.
Bioprocesses are involved in producing different pharmaceutical products. Complicated dynamics, nonlinearity and non-stationarity make controlling them a very delicate task. The main control goal is to get a pure product with a high concentration, which commonly is achieved by regulating temperature or pH at certain levels. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The novel approach used here is to use the inverse of penicillin concentration as a cost function instead of a common quadratic regulating one in an optimization block. The result of applying the obtained controller has been displayed and compared with the results of an auto-tuned PID controller used in previous works. Moreover, to avoid high computational cost, the nonlinear model is substituted with neuro-fuzzy piecewise linear models obtained from a method called locally linear model tree (LoLiMoT).
Bioprocesses which are involved in producing different pharmaceutical products may conveniently be classified according to the mode chosen for the process: either batch, fed-batch or continuous. From the control engineer's viewpoint they are fed-batch processes, which present the greatest challenge to get a pure product with a high concentration. Complicated dynamics, nonlinearity and non-stationarity make controlling them a very delicate task. pH control of bioreactors has been an interesting problem from both implementation and controller design points of view. This is particularly true if the complex microbial interactions yield significant nonlinear behavior. When this occurs, conventional control strategies may not succeed and more advanced strategies need to be suggested. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The approach used here is to use quadratic cost function for pH regulation, while taking into account control signal fluctuations in the optimization block. The result of applying the obtained controller and also its sensitivity to disturbance have been displayed and compared with the results of an auto-tuned PID controller used in previous works. The merit of this method is its low computational cost of solving the optimization problem, while leading to a closed form controller as well.
Due to uncertainties in system modeling as well as system parameters, current excitation systems are unable to perform quite satisfactorily over a wide range of operating conditions. In this paper a QFT-based excitation robust control is proposed which the above mentioned uncertainties are, somehow, considered. The Horowitz second method is employed in the design of the nonlinear QFT controller.
In this paper, pure proportional guidance in 3-D space is first explained with a new perception. The main idea is based upon the distinction between angular rate vector and rotation vector conceptions. In this innovation, the emphasis is based upon the selection of line of sight coordinates and comparison between the two available views for choosing this system. Then, using an additional term, an improvement to this law is made. This term compromises a cross range compensator, which is used to provide first fluctuations for obtaining convergent estimates of state variables. Then, a state-space description within the improved spherical coordinate system has been offered. The available measurements in this system have been chosen with regard to the considered practical points. Then, the issue of range-to-target estimation is proposed and some non-linear filters, such as extended Kalman filter, unscented Kalman filter, particle filter, EKF particle filter, and UKF particle filter in the modified spherical coordinates have been used. Simulations indicate that the proposed tracking filters in conjunction with the dual guidance law are able to provide the convergence of the range estimation for both maneuvering and non-maneuvering targets.
In this study, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL strategy consists of intelligent and conventional controllers in feedforward and feedback paths, respectively. In this strategy, a conventional feedback controller (CFC), i.e. proportional, integral and derivative (PID) controller, is essential to guarantee global asymptotic stability of the overall system; and an intelligent feedforward controller (INFC) is adopted to learn the inverse of the controlled system. Therefore, when the INFC learns the inverse of controlled system, the tracking of reference signal is done properly. Generally, the CFC is designed at nominal operating conditions of the system and, therefore, fails to provide the best control performance as well as global stability over a wide range of changes in the operating conditions of the system. So, in this study a supervised controller (SC), a lookup table based controller, is addressed for tuning of the CFC. During abrupt changes of the power system parameters, the SC adjusts the PID parameters according to these operating conditions. Moreover, for improving the performance of overall system, a recurrent fuzzy neural network (RFNN) is adopted in INFC instead of the conventional neural network, which was used in past studies. The proposed FEL controller has been compared with the conventional feedback error learning controller (CFEL) and the PID controller through some performance indices.
The purpose of this paper is to formulate truth-value assignment to self-referential sentences via Zadeh's truth qualification principle and to present new methods to assign truth-values to them. Therefore, based on the truth qualification process, a new interpretation of possibilities and truth-values is suggested by means of type-2 fuzzy sets and then, the qualification process is modified such that it results in type-2 fuzzy sets. Finally, an idea of a comprehensive theory of type-2 fuzzy possibility is proposed. This approach may be unified with Zadeh's Generalized Theory of Uncertainty (GTU) in the future.
Current developments in the aerospace flight devices have led to a control system being designed in the presence of elastic behaviour. However, there are several ways to reduce the destructive effects of vibration in flexible systems. In this paper, a practical approach called ‘rigid model reference’ is extended to two vibration modes based on the gradient method. Furthermore, the existence of two dominant bending vibration modes in the output of measurement devices leads to a redesign of the control system. Robust stability of the new algorithm is investigated by using Kharitonov theorem. Simulation results illustrate considerable reduction of vibration effects on the output of measurement system considering the first and the second bending vibration modes.
Gain scheduling is one of the most popular nonlinear control design approaches which has been widely and successfully applied in fields ranging from aerospace to process control Despite the wide application of gain scheduling controllers, theme is a notable lack of analysis on the stability of these controllers. The most common application of these kinds of controllers is in the field of flight control and autopilots. The main goal of this paper is to apply a methodology to prove stability of a gain scheduled controller used in directing Skid-to-Turn missiles. One of the most widespread applications of gain scheduling controller is the main problem of this paper. To design the controller we use pole placement in state feedback controllers and a kind of innovative interpolation to reduce jumping in gains related to changing the flight conditions. Finally we utilize root locus and Kharitonov’s Theorem to prove stability of the linearized plant.The presented approach for stability analysis is distinctive in the literature.
In this paper, two types of multiple-model adaptive controllers are practically evaluated on a laboratory-scale pH neutralization process. The first one is supervisory switching multiple-model adaptive controller (SMMAC) whose model bank is fixed and selected a priori, and another one is a controller based on multiple models, switching, and tuning strategy (MMST) which uses the possibility of model bank tuning. In addition to investigation of the effect of tuning, the advantage of a disturbance rejection supervisor is studied. Various experiments and exhaustive numerical analyses are provided to assess the abilities of the proposed algorithms.
Thyroid gland produces thyroid hormones to help the regulation of the body's metabolism. The abnormalities of producing thyroid hormones are divided into two categories. Hypothyroidism which is related to production of insufficient thyroid hormone and hyperthyroidism related to production of excessive thyroid hormone. Separating these two diseases is very important for thyroid diagnosis. Therefore support vector machines and probabilistic neural network are proposed to classification. These methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper feature selection is argued as an important problem via diagnosis and demonstrate that GAs provide a simple, general and powerful framework for selecting good subsets of features leading to improved diagnosis rates. Thyroid disease datasets are taken from UCI machine learning dataset.
This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network-based Fuzzy Inference System (ANFIS) as a system identifier. The proposed hybrid learning algorithm is based on the particle swarm optimization (PSO) for training the antecedent part and the extended Kalman filter (EKF) for training the conclusion part. Lyapunov stability theory is used to study the stability of the proposed algorithm. Comparison results of the proposed approach, PSO algorithm for training the antecedent part and recursive least squares (RLSs) or EKF algorithm for training the conclusion part, with the other classical approaches such as, gradient descent, resilient propagation, quick propagation, Levenberg–Marquardt for training the antecedent part and RLSs algorithm for training the conclusion part are provided. Moreover, it is shown that applying PSO, a powerful optimizer, to optimally train the parameters of the membership function on the antecedent part of the fuzzy rules in ANFIS system is a stable approach which results in an identifier with the best trained model. Stability constraints are obtained and different simulation results are given to validate the results. Also, the stability of Levenberg–Marquardt algorithms for ANFIS training is analyzed.
In this paper a Mamdani type fuzzy system and an adaptive network based fuzzy inference system (ANFIS) are presented for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. The architecture and learning procedure ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. It is shown that both these controllers can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of nonlinear mathematical model of the system.
Brain Computer Interface (BCI) is a technology that developed over the last three decades has provided a novel and promising alternative method for interacting with the environment. BCI is a system which translates a subject's intentions into a control signal for a device, e.g., a computer application, a wheelchair or a neuroprosthesis. Electroencephalogram-based BCI has become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Removing artifacts produced in Electroencephalogram (EEG) data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG analysis. In this research, for artifact rejection, EEG data are filtered to the frequency range between 8 and 32 Hz with a butterworth band-pass filter. Finally two different structures of neural network and a support vector machine used to classify features that are extracted by Hilbert and Wavelet transform.
In this paper, a multiple models, switching, and tuning control algorithm based on poleplacement control is studied. Drawbacks of the algorithm in disturbance rejection are discussed, and a novel supervisor to enhance the decision-making procedure is developed. The modified algorithm is evaluated in a simulation study for a nonlinear pH neutralization process. Comparison results are provided to evaluate the performance and robustness characteristics of the proposed algorithm.
In this paper, after defining pure proportional navigation guidance in the 3-dimensional state from a new point of view, range estimation for passive homing missiles is explained. Modeling has been performed by using line of sight coordinates with a particular definition. To obtain convergent estimates of those state variables involved particularly in range channel and unavailable from IR trackers, nonlinear filters such as sequential U-D extended Kalman filter and Unscented Kalman filter in modified spherical coordinate combined with a modified proportional navigation guidance law are proposed. Simulation results indicate that the proposed tracking filters in conjunction with the dual guidance law are able to provide the convergence of the range estimate for both maneuvering and nonmaneuvering targets.
In this paper, we design a neurofuzzy controller to control several variables of a rotary cement kilns. The variables are back-end temperature, pre-heater temperature, oxygen content and CO2 gas content of the kiln. The fuzzy control system, as an advanced control option for the kilns, is intended to minimize the operator interaction in the control process. The proposed fuzzy controller uses a neural network to optimize TSK-type fuzzy controller. Since there is no generally applicable analytical model for cement kilns, we use the real data derived from Saveh cement factory for the plant identification. A model, which is very similar to the real plant, is identified then; and the identified model is used for control design and simulations. Extensive simulation studies justify the effectiveness and applicability of the proposed control scheme in intelligent control of cement plant.
In this study, a new approach to solve the Sylvester equation, AX+ XA=-ВС is derived. The calculated cross-Gramian matrix, which results from the Sylvester equation, proposes a new input-output pairing analysis for stable multivariable plants. This new approach is based on the cross-Gramian matrix of SISO elementary subsystems built from the original MIMO plant and the main advantage of the method is its simplicity to choose the best input-output pair, though, it considers the plant dynamic properties.
This paper introduces a new scheme for building a clean room with no unwanted sound in it. Resent efforts for building such rooms always encounter problems because of the stochastic nature of these systems. A real clean room not only faces some non-moving sources, but also faces some moving ones, that probably the sound source is a moving one in reality. In order to solve the problem of motion of those moving sources, one must design a method to solve this problem which we definitely encounter with. Active noise cancellation needs the direction of transmission of the unwanted sound in order to reduce its effect. Hence, these rooms need an identifier, which we suggest a clean room identification algorithm in this paper. Recent efforts for building these kind of room, always suppose the non-moving sound sources which we may encounter a moving one in every day life. The other problem is the mutual effect of each source on the other microphones. Those non-moving sources just cause mutual effect on all microphones and compel us using a separator. Therefore, these systems need a separator, which we use BSS algorithms in order to reach this aim. Finally, simulation results are provided to illustrate the main points.
Pairing is the first step of decentralized controller design procedure in multi-input multi- output (MIMO) processes. In spite of considerable efforts dedicated to this problem, most of the known pairing techniques are offline algorithms and fail to decide when dealing with high dimensional and/or time varying processes and adaptive control applications. In this article, normalized effective relative gain array (NERGA) is introduced as an effective automatic pairing method and is employed in a new adaptive decentralized PID control strategy.
This paper presents a robust adaptive control design methodology for multi-input multi-output (MIMO) plants based on Quantitative Feedback Theory (QFT) and Externally Excited Adaptive System (EEAS), both of which are the novel ideas of Horowitz. Self Oscillating Adaptive Systems (SOAS) are proposed to mainly overcome the problem of large gain variations, which is important in certain applications. To further improve the SOAS design, the idea of EEAS was developed. Finally, combined QFT and EEAS proposed a robust adaptive controller for SISO uncertain plants. However, due to the complex design nature of the proposed combined methodology and the difficulty of an optimal design, this line of Horowitz's research was not followed further. In this paper, to overcome the above mentioned problems the design procedure is reformulated as a set of cost functions and constraints. Genetic Algorithms are then used to solve the optimal design. Also, QFT/EEAS design is extended to multivariable uncertain plants. Sufficient conditions are derived to assure the achievement of given off-diagonal performance. Then, the given main channel performance could be achieved by using SISO QFT/EEAS method. Simulation studies indicate the effective performance of the proposed QFT/EEAS MIMO design methodology. It is shown that the proposed approach can handle large plant parameter uncertainties with lower loop bandwidths.
This paper presents the adaptive control of chaotic systems, which are nonlinear in parameters (NLP). A method based on Lagrangian of an objective functional is used to identify the parameters of the system. Also this method is improved to result in better rate of convergence of the estimated parameters. Estimation results are used to calculate the Lyapunov exponents adaptively. Finally, the Lyapunov exponents placement method is used to assign the desired Lyapunov exponents of the closed loop system. Simulation results are provided to show the effectiveness of the results.
Inherent nonlinearity of pH processes causes that they are recognized as an appropriate test bench for evaluation of advanced controllers. Because of special characteristics of them, it is evident that adaptive controllers outperform others. This paper presents a comparison between a conventional adaptive controller and a switching multiple-model adaptive one in both regulation and disturbance rejection points of view. A disturbance rejection supervisor is designed to improve the performance of the adaptive controllers in the presence of unmeasured disturbances. A laboratory scale pH process is used as an application example.
Tracking moving objects in variable cluttered environments is an active area of research. It is common to use some simplifying assumption in such environments to facilitate the design. In this paper a new method for simulating the completely non-Gaussian cluttered environments is presented. The method is based on using the variable variance of process noise as a description of variability in such environments. The key objective is to find an effective algorithm for tracking a single moving object in variable cluttered environments, with utilization of the presented method. The new methodology is presented in two steps. In the first step we compare the accuracy of estimators in tracking a moving object, and in the second step, the goal is to find the best algorithm for tracking a single moving target in variable cluttered environments.
Control based on multiple models (MM) is an effective strategy to cope with structural and parametric uncertainty of systems with highly nonlinear dynamics. It relies on a set of local models describing different operating modes of the system. Therefore, the performance is strongly depends on the distribution of the models in the defined operating space. In this paper, the problem of on-line construction of local model set is considered. The necessary specifications of an autonomous learning method are stated, and a high-level supervisor is designed to add an appropriate model to the available model set. The proposed algorithm is evaluated in a simulated pH neutralization process which is a highly nonlinear plant and composed of both abrupt and large continuous changes. The preference of the multiple-model approach with learning ability on a conventional
Considering the need of an advanced process control in cement industry, this paper presents an adaptive model predictive algorithm to control a white cement rotary kiln. As any other burning process, the control scenario is to expect the controller to regulate the temperature and the period of baking a fixed quantity of raw material as desired, as well as to have the concentration of the combustion gases under control. To achieve these goals, this work presents a strategy which includes multivariable online identification of the kiln process and a constrained generalized predictive controller. An MLP neural network model derived from real plant data of Saveh cement factory in Iran is used as the kiln process simulator. The control efforts are made taken into account the operating constraints. At last the proposed control strategy is modified so as to gain good disturbance rejection ability.
Current state-of-the-art approaches for control of hybrid systems face two main important challenging problems which are stabilization and computational complexity. This paper aims at improving a special strategy i.e. predictive ontrol for a special class of hybrid systems i.e. mixed logical dynamical systems. For mixed logical dynamical ystems as a main class of hybrid systems, the only existing way to ensure the closed loop stability of predictive controllers is to use a terminal state equality constraint in the successive optimization problems. Limitations caused by this type of constraint have been discussed. Contractive predictive control is proposed as a good alternative which assures the closed loop stability in a more feasible manner. As a Lyapunov function, the L1 norm of the state vector is enforced to shrink in successive optimization steps. A suboptimal version of contractive MPC scheme has been proposed which reduces the computational complexity of the control problem while preserving the stability
Using the assignment technique of operations research, it is possible to obtain optimal controllable and observable pairs based on gramian measure for decentralised control of MIMO plants. The advantages of using assignment method for both open and closed loop performance in a MIMO plant are discussed in this paper.
In this paper a novel approach is proposed to solve a decentralized control problem to stabilize a multivariable system and attenuate the interconnections between its subsystems. To satisfy these conditions, an ut feedback controller is designed by solving an H∞ control problem. The designed controller is applied to a practical multivariable Flow-Level plant to show the effectiveness of the proposed methodology. The time delays, transmission zero, two different time constants, and the model uncertainties are the main problems in this plant.
This Letter is concerned with bifurcation and chaos control in scalar delayed differential equations with delay parameter τ. By linear stability analysis, the conditions under which a sequence of Hopf bifurcation occurs at the equilibrium points are obtained. The delayed feedback controller is used to stabilize unstable periodic orbits. To find the controller delay, it is chosen such that the Hopf bifurcation remains unchanged. Also, the controller feedback gain is determined such that the corresponding unstable periodic orbit becomes stable. Numerical simulations are used to verify the analytical results.
Abstract A method of using particle swarm optimization (PSO) algorithm to design electromagnetic absorber is presented. To demonstrate effectiveness of the PSO algorithm three different design cases are optimized. To reduce the local minimum traps, a modified local search strategy is employed. Each design problem is optimized using genetic algorithm (GA) and four variants of PSO algorithms, namely global PSO (gbest), local PSO (lbest), comprehensive learning PSO (CLPSO), and modified local PSO (MLPSO). The results clearly show that the MLPSO is a robust, fast, and useful optimization tool for designing absorbers. A seven-layer absorber achieved by this method has reflection coefficient below 18.7 dB from VHF to 20 GHz.
In the present research, a non-linear controller is designed for the control of an active suspension system for a half-model vehicle, using a Fuzzy Neural Network (FNN) along with Feedback error learning. The purpose in a vehicle suspension system is reduction of transmittance of vibrational effects from the road to the vehicle chassis, hence providing ride comfort. This requires a minimum reduction in road contact along rough roads. In addition, the role of the suspension system in vehicle control along a curved route and in accelerating and braking is quite evident. To accomplish this, one can first design a PD controller for the suspension system, using a classic control method and use it to train a fuzzy controller. This controller can be trained using the PD controller output error on an online manner. Once trained, the PD controller is removed from the control loop and the neuro-fuzzy controller takes on. In case of a change in the parameters of the system under control, the PD controller enters the control loop again and the neural network gets trained again for the new condition. Important characteristics of the proposed controller is that no mathematical model is needed for the system components, such as the non-linear actuator, spring, or shock absorber, and that no system Jacobian is needed. The performance of the proposed FNN controller is compared with that of the PD controller through simulations. The results show that the proposed controller is indeed capable of meeting the stated control requirements.
In this method, firstly, the frequency responses of system in a few points are predicted and are compared with the frequency response of the model reference that is the proper loop gain function. In the next step, a second order Controller for compensating is designed. Finally, a benchmark for the convergence of the real loop to the reference function and the stability of the closed loop is introduced. As the convergence of the response is adequate in a limited band the structural information of the system such as order of the system, order and number of delays is not necessary. The application of this method is in control of high order system and the systems with delayed response.
Neural Network Model Predictive Control (NN-MPC) combines reliable prediction of neural network with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. It is shown that this structure is prone to steady-state error when external disturbances enter or actual system varies from its model. In this paper, these model uncertainties are taken into account using a disturbance model with iterative learning which adaptively change the learning rate to treat gradual effect of the model mismatch differently from the drastic changes of external disturbance. Then, a high-pass filter on error signal is designed to distinguish disturbances from model mismatches. Practical implementation results as well as simulation results demonstrate good performance of the proposed control method.
In This paper, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL controller consists of neural network feedforward controller (NNFC) and conventional feedback controller (CFC), where the CFC is essential to guarantee global asymptotic stability of the overall system. Also, for improved the performance of system the dynamic neural network (DNN) is adopted in NNFC instead of conventional neural network. This neural network has dynamic in its structure and consists of two units: inhibitory and excitatory unit. The proposed FEL controller has been compared with the conventional FEL (CFEL) controller and the PID controller through some performance indices.
Minimal Stopping Distance, Guaranteed Steering ability and Stability are the three most important proposes in Anti-lock Braking System(ABS)realm. The ABS system is nonlinear, time variant, multivariable and uncertain. Up to now several researches have been done on ABS control system, but nearly all of them are intricate and expensive. In this paper we exploit a multivariable technique in linear control to attack the problem, which is Designed Linear Control with Multivariable Technique. The Optimal Eigenstructure Assignment with Genetic Algorithm(GA) method is also applied. Simulation and comparison studies are used to show the effectiveness of the proposed methods.
The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.
In this paper two robust controllers are designed for a practical Process Trainer Level plant. The system nonlinearity, time delay and change of parameters are the main problems in design of a desired controller for this plant. To design a controller, the linear models of the system and the disturbance models at different operating points are derived. Then, a parametric uncertainty profile is obtained by system identification strategies which is used in QFT control design. Indeed, for H∞ control design a multiplicative unstructured model is extracted from the parametric uncertainty. All constraints in control design, disturbance rejection and control signal are derived. Based on these constraints, appropriate controllers are determined. To improve robust performance μ-Synthesis with DK iteration is used. Finally all results are compared by applying the different controllers to the plant.
In this study, a new group method of data handling (GMDH) method, based on adaptive neurofuzzy inference system (ANFIS) structure, called ANFIS-GMDH and its application for diabetes mellitus forecasting is presented. Conventional neurofuzzy GMDH (NF-GMDH) uses radial basis network (RBF) as the partial descriptions. In this study the RBF partial descriptions are replaced with two input ANFIS structures and backpropagation algorithm is chosen for learning this network structure. The Prima Indians diabetes data set is used as training and testing sets which consist of 768 data whereby 268 of them are diagnosed with diabetes. The result of this study will provide solutions to the medical staff in determining whether someone is the diabetes sufferer or not which is much easier rather than currently doing a blood test. The results show that the proposed method performs better than the other models such as multi layer perceptron (MLP), RBF and ANFIS structure.
This study has developed classical and hybrid controllers for control of magnetic levitation system. Sliding mode and PID controllers are proposed as a classical controllers and neural network based controller is used for controlling a magnetic levitation system. Adaptive neural networks controller needs plant`s Jacobain, but here this problem solved by sliding surface and generalized learning rule in case to eliminate Jacobain problem. The simulation results show that these methods are feasible and more effective for magnetic levitation system control.
In this paper, we use system identification methods for abnormal condition detection of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. A novel approach is used in order to estimate the delays of the input channel of the kiln. By means of that, the identification task gets easier and the results are more accurate. To identify the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Finally, a model for the healthy mode of the kiln is obtained through which it is possible to detect abnormal conditions in the process. We distinguished two common abnormal conditions in kiln and another one which was not characteristically known for cement experts as well.
In this paper with reference to analytical results of different well-known relay feedback methods, we illustrate a main deficiency in parameter estimation of processes with a small ratio of time delay to time constant. Then to rectify this problem we introduce a modified relay feedback structure with additional delay to estimate the parameters of the FOPDT transfer function of the system. The significance of this method lies in the fact that many industrial plants perform fairly such as FOPDT systems, and a wide range of processes have negligible dead time versus their long constant time. Also, the estimated FOPDT transfer function from proposed relay feedback test can be used as a priori knowledge in advanced control strategies which need a FOPDT model of the system. The method is straightforward and simulation results illustrate the effectiveness, and simplicity of the proposed method.
Decentralized control structure is widely employed in many industrial multivariable processes. In this approach, control structure design and in particular input–output pairing is a vital stage in the design procedure. There are several powerful methods to select the appropriate input–output pair in linear multivariable plants. However, in the face of plant uncertainties, the input–output pairs can change. Input–output pairing problem, in the presence of uncertainties, and its consequences on the pairing problem have not been widely addressed. In this paper, Hankel interaction index array is used to choose the appropriate input–output pair and a new method is proposed to compute Hankel interaction index array, which reduces the computational load. Also, a theorem will be presented to show the effect of additive uncertainties on input–output pairing of the process. An upper bound on the element variations of Hankel interaction index array of the additive uncertainties in state space framework is given to show the possible change in input–output pairing. Finally, two typical processes are employed to show the main points of the proposed methodology.
In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm which was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Another genetic algorithm is used to repair some paths which collide with obstacles. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.
The paper deals with problem of estimating input channel delay in nonlinear system with a model-free approach. The proposed method is based on Lipschitz theory. It is an extension to the Lipschitz method which was proposed for determining the order of a model. Our algorithm consists of two parts which in the first one estimation is made on the proper number of dynamics on the input and in the second part the pure delay of the input is obtained. The method is applied for estimation of the delay of two different models and the estimation was as accurate as possible.
In this study, designing of multi-objective (MO) proportional, integral and derivative (PID) controller for load frequency control (LFC) based on adaptive weighted particle swarm optimization (AWPSO) has been proposed. Unlike single objective optimizations methods, MO optimization can find different solutions in a single run and we can select appropriate and desirable solution based on valuation to the objects. In this study for PID controller design, overshoot/undershoot and settling time are used as objective functions for MO optimization in LFC problem. So that various solutions with different overshoot/undershoot and settling time obtained. From these different PID parameters, one can select a single solution based on valuation to objects and as well as system constraints, reliability etc. The proposed method is used for designing of PID parameters for two area interconnected power system. From the simulation results, efficiency of proposed controller design can be seen.
In this paper designing of multi-objective PID controller for load frequency control (LFC) based on adaptive weighted particle swarm optimization (AWPSO) has been proposed. Conventional methods such as Ziegler-Nichols and Cohen-Coon are based on trial-and- error and their best performances are achieved for first-order process. Single-objective population based methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have only one solution in a single run. Unlike single objective methods, multi- objective optimization can find different solutions in a single run. In the proposed method, overshoot/undershoot and settling time are used as objective functions for multi-objective optimization. The proposed method is used for designing of PID parameters for two area interconnected power system.
Neural network Based controller is used for controlling a magnetic levitation system. Feedback error learning (FEL) can be regarded as a hybrid control to guarantee stability of control approach. This paper presents simulation of a magnetic levitation system controlled by a FEL neural network and PID controllers. The simulation results demonstrate that this method is more feasible and effective for magnetic levitation system control.
Bioprocesses, which are involved in producing different antibiotics and other pharmaceutical products, may be conveniently classified according to the mode chosen for the process: either batch, fed-batch or continuous. From the control engineer's viewpoint it is the fed-batch processes, however, which present the greatest challenge to get a pure product with a high concentration. To achieve this goal, control of the following parameters has significant importance dealing with these processes: temperature, pH, dissolved oxygen (DO2). Bioprocesses have complicated dynamics. Hence, their control is a delicate task; Nonlinearity and non-stationarity, which make modeling and parameter estimation particularly difficult perturbs such processes. Moreover, the scarcity of on-line measurements of the component concentrations (essential substrates, biomass and products of interest) makes this task more sophisticated. In this paper, Model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor has been developed. MPC is performed via determining the control signal by minimizing a cost function in each step. The results of this controller to maximize penicillin concentration have been displayed and also compared with the results of auto-tuned PID controller used in previous works.
In the present paper, an adaptive control approach for a flexible launch vehicle is proposed. This approach makes use of gain scheduling and model reference adaptive filter methods to control the flexible behaviours of the launch vehicle structure, which can lead to control system stability degradation. Applying this adaptive controller to an eight-degrees-of-freedom flexible launch vehicle, gives stable and desired responses. Because the designed adaptive controller adjusts only one single parameter and is designed based on the MIT (Massachusetts Institute of Technology) rule, it is simpler and faster than the other approaches. Therefore, this newly designed algorithm is less central processing unit-intensive, which makes it easier to implement in real-time applications.
A multiple-model adaptive controller is developed using the Self-Organizing Map (SOM) neural network. The considered controller which we name it as Multiple Controller via SOM (MCSOM) is evaluated on the pH neutralization plant. An improved switching algorithm based on excitation level of plant has also been suggested for systems with noisy environments. Identification of pH plant using SOM is discussed and performance of the multiple-model controller is compared to the Self Tuning Regulator (STR) controller.
In this paper a Neural Predictive Controller (NPC) designed to control a broad class of process systems. Neural network identification yields nonlinear global model of the unknown system. LevenbergMarquardt (L-M) optimization method is used to find optimal control signal to minimize future errors of the objective function of predictive controller. Inequality constraints of actuators are added to the objective function through a penalty term which increases drastically as it approaches the limitations. To use the controller for wide range of process systems, an initial phase runs before the main controller to determine parameters. This phase moves the system output to operating point and applies PID controller with APRBS reference signal. The gathered data are used to estimate parameters such as pure delay, prediction horizon, control coefficient and identification order. To validate the approaches, the controller has implemented in level, pressure and flow pilot plants and compared with conventional controller which shows faster and smoother tracking results.
Closed loop identification of nonlinear model and control of a laboratory helicopter using genetic algorithm is proposed in this paper. The derived model has a nonlinear structure. Using the previous results of the physical modeling of the studied plant, a nonlinear model is considered based on the physical dynamics of the system. However, there is no need to perform numerous physical experiments to estimate the model parameters. Instead, genetic algorithm as a nonlinear optimization technique is used to obtain the parameters of the model. Therefore, the advantage of both modeling and identification methods are employed. In the next step, the parameters of a multi input-multi output (MIMO) PID controller for the derived model will be tuned by GA using the obtained nonlinear model as a simulator of the plant. Applying the controller to both the real plant and the simulation model, the accuracy of the model and the performance of the controller is examined. The results demonstrate that the achieved model accurately fits to the behavior of the real plant and the controller designed based on this model, can control the real system appropriately.
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS) and a new type of particle swarm optimizers (PSO). The previous works emphasized on gradient base method or least square (LS) based method. This study applied one of the swarm intelligent branches, PSO. The hybrid method composes Fuzzy PSO with recursive least square (RLS) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from Fuzzy Systems method and using Fuzzy rules for tuning PSO parameters during training algorithms. The simulation results show that in comparison with current gradient based training, and authors previous hybrid method the proposed training have a good adaptation to complex plants and train less parameter than gradient base methods.
In this paper, a model predictive control scheme for a class of nonlinear systems is presented. In the proposed algorithm, the new cost function for MPC is defined. This cost function is inspired by the structure of passivity-based control. By simple tuning of weighting matrices, the asymptotic stability is guaranteed. Moreover, a closed-form solution to the optimal control problem is calculated via representing the nonlinear system in the state-dependent coefficient form of the state-space model. This point is of great importance in online applications. To demonstrate its efficiency, the passivity-based structured MPC is applied to control a rotational motion of a rigid body.
In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm that was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.
Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW).
Performance assessment and monitoring of control systems can be used to improve the performance of industrial processes. In this paper, a novel relay feedback based method for monitoring and automatic retuning of a class of proportional-integral (PI) controllers is proposed for the systems with gain nonlinearity. For performance assessment of the closed loop system, a time domain evaluation criteria based on the integral of the absolute value of the error (IAE) and the normalized pick of the error in setpoint (SP) changes are presented. Simulation results on the highly nonlinear pH process have shown the effectiveness and feasibility of this method.
In this study we predict air pollution data by using Multi Layer Percepteron, Time Delay Line, Gamma and ANFIS by gradient free learning methods. This paper, using real data for Arak city during Oct 2003, the following pollution parameters are analysed: Co (Carbon Monoxide), PM10 (Particulate Matter). This analysis is carried out in two stages: Predictability analysis using Lyapanov, Exponent, Correlation Dimension and Rescaled Range Analysis (R/S), Prediction using Multi layer perceptron, Time delay line, Gamma and ANFIS. Also, a comparative study is performed using the different methods employed and prediction results are provided to show the effectiveness of the predictions.
This paper, describes a hybrid control method to control a flexible joint. Dynamic equation of the system has been derived. The designed controllers consist of two parts: classical controller, which is a Linear Quadratic Regulation (LQR), and a hybrid controller, utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN). The RBFNN is trained during the control process and it is not necessary to be trained off-line.
This paper has developed a sliding mode controller (SMC) based on a radial basis function model for control of Magnetic levitation system. Adaptive neural networks controllers need plant's Jacobain, but here this problem solved by sliding surface and generalized learning rule in case to eliminate Jacobain problem. The simulation results show that this method is feasible and more effective for Magnetic levitation system control.
This study suggests new learning laws for Adaptive Network based Fuzzy Inference System that is structured on the basis of TSK type III as a system identifier. Stable learning algorithms for consequence parts of TSK type III rules are proposed on the basis of the Lyapunov stability theory and some constraints are obtained. Simulation results are given to validate the results. It is shown that instability will not occur for learning rates in the presence of constraints. The learning rate can be calculated online from the input–output data, and an adaptive learning for the Adaptive Network based Fuzzy Inference System structure can be provided.
As a way to reduce the on-line computational burden, explicit solution to the problem of optimal control for some classes of hybrid systems can be found by reformulating the problem as multi-parametric MILP problems. The main contribution of this paper is the introduction of an approximation algorithm for solving a general class of mp-MILP problems. The algorithm wisely selects those binary sequences which make important improvement in the objective function if considered. It is shown that considerable reduction in computational complexity could be achieved by introduction of adjustable level of suboptimality. So a family of suboptimal controllers would be obtained for which the level of error and complexity can be adjusted by a tuning parameter. Several important theoretical results about approximate solutions to the mp-MILP problem are presented. It is shown that no part of the parameter space is lost during the approximation. Also it is proved that the error in the achieved approximate solutions is monotonically increasing function of the tuning parameter. The reduced complexity achieved by the proposed approach is clarified through an illustrative example.
Objective: To assess the validity of diagnoses obtained with the Iranian version of the Structured Clinical Interview for DSM-IV (SCID-I).Methods: This study was undertaken in two stages: (a) translation of SCID-I into Persian (Iranian language), (b) assessing the validity of the Persian version in a sample of Iranian patients. We recruited 299 psychiatric patients- including inpatients and ambulatory cases- from 3 teaching hospitals. A trained SCID interviewer administered the SCID and then two psychiatrists developed a consensus diagnosis, using data from multiple sources. Results: The degree of agreement between SCID interviews and psychiatrists' diagnosis ranged from "moderate" for obsessive-compulsive and major depressive disorders to "good" for bipolar disorder and schizophrenia. With the psychiatrists’ diagnosis used as the gold standard, the SCID-based diagnosis showed high specificity and moderate to high sensitivity for most psychiatric diseases. Conclusion: The results of this study indicate that the Iranian version of the SCID is a valid instrument for diagnosis in clinical settings.
This paper uses potential clustering approach to perform online fuzzy clustering. This method is an improvement of the subtractive clustering which is a noniterative clustering algorithm and so is suitable for online applications. In Spite of all capabilities of the potential clustering, this method suffers from a major disadvantage. The number of clusters grows fast when the sensitivity of the algorithm is increased. In this article an innovative technique has been proposed to reduce the number of clusters. The proposed method is applied to the Macky-Glass benchmark. It is shown although the number of clusters is reduced; the resulting performance will not be affected.
In this paper, a new stabilizing control law with respect to a control Lyapunov function (CLF) is presented. This control law is similar to the pointwise min-norm control law. This control law is designed to maximize the angle between the gradient of the control Lyapunov function and the time derivative of the state vector at the state trajectory, which is defined in what follows as the “pointwise maximum angle control law.” A comparison with the pointwise minnorm control law is provided. A criterion of the stability performance of control laws that are designed with respect to a CLF is presented. Also, by proposing the concept of the “eigen-angle” for real square nonsingular matrices, the stabilization of some nonaffine nonlinear systems, and the construction of a CLF for such systems are reduced to the construction of CLFs for affine nonlinear (linear) systems. Finally, simulation results are provided to show the effectiveness of the proposed methodologies.
Particle swarm optimization (PSO) as a novel computational intelligence technique, has succeeded in many continuous problems. But in discrete or binary version there are still some difficulties. In this paper a novel binary PSO is proposed. This algorithm proposes a new definition for the velocity vector of binary PSO. It will be shown that this algorithm is a better interpretation of continuous PSO into discrete PSO than the older versions. Also a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.
Rotary Kiln is the central and the most complex components of cement production process. The first point at the beginning of the process, which is called back-end, is the calcining zone of the kiln and has a significant role on the quality of the clinker. In this paper to control the back-end temperature of a rotary kiln, we propose a fuzzy controller based on the operator's behaviors. We concentrate on how we can control the back-end temperature of a rotary kiln using a fuzzy controller. The work presented in this paper, is to use the advantages of fuzzy logic techniques to propose and develop a supervisory control system for the cement plant process having complex dynamics. The performance of the proposed controller is investigated by applying it to a simulator of the plant, derived from an MLP neural network model. Finally, the controller's behavior is evaluated in a disturbance environment. Simulation results show that the functionality of the controller causes a smooth behavior in both controller and model outputs.
Balanced realization has the advantage of producing some valuable information on controllability and observability (C/O) of the plant. This specification was used in pairing of MIMO plants to some SISO subplants. Using balanced realization, the pairs with better C/O are selected. In this paper the problem of pairing based on balanced realization is interpreted as an assignment problem. Therefore the Hungarian algorithm can utilize to solve the pairing problem. The algorithm is fully systematic and may be utilize in online and adaptive pairing. The pairing algorithm is also developed to reject any undesired pair like uncontrollable and/or unobservable pairs. With some modification, it is also applied to nonsquare plants.
BACKGROUND: Considering reports on the associations of symptoms of anxiety disorders with multiple sclerosis (MS), this study aimed to 1) further evaluate various anxiety disorders systematically presenting in patients with MS and 2) compare the results with a control group. METHODS: To assess anxiety disorders in patients with MS in a case-control study, 85 registered patients in the Iranian Multiple Sclerosis Society (IMSS) were randomly selected according to the inclusion criteria. A group of healthy individuals whose age and gender were matched with the case group were also selected. Both groups underwent a clinical interview based on DSM-IV diagnostic criteria. RESULTS: Frequency of diagnosis of all anxiety disorders in the two groups was 22.4% and 7.1%, respectively, indicating a statistically significant difference. Frequency of obsessive-compulsive disorder (OCD) was significantly higher in the case group (P<0.05). Relation of university education with the diagnosis of generalized anxiety disorder was significant too (P<0.05). CONCLUSIONS: OCD in patients with MS was more frequently observed than in the control group.
Input/output pairing is an important task in control of a MIMO process by some SISO controllers. Relative gain array (RGA) is the most important method to find the best pairing. But selection of pairs based on RGA is an offline algorithm which needs some human decision. In this article, Normalized RGA (NRGA) matrix is introduced through the combination of the RGA matrix and its selection rules. Using NRGA, pairing problem can be interpreted as an assignment problem. The well known Hungarian algorithm is applied to the above problem to obtain the optimal pairing.
A comprehensive step-by-step approach to the construction of a global fuzzy TSK model for a real SISO nonlinear system in closed loop is proposed. First the overall existing nonlinear distortions are evaluated, and then sub optimal experiments are designed so that the impacts of distortion are minimized. Such experiments can result in models which are consistent local estimations of the true underlying linear system at different operating points. Finally by suitable fuzzy combination of extracted open loop local models, a global fuzzy simulation model is constructed. Employing a quasi tailor-made parameterization, a model refinement can be carried out by trimming the membership functions through any optimization algorithms. In opposite of the conventional tailor-made parameterization algorithm, the stability of this modified algorithm is guaranteed.
In this paper a decoupled sliding-mode with fuzzy neural network controller for a nonlinear system is presented. To divided into two subsystems to achieve asymptotic stability by decoupled method for a class of three order nonlinear system. The fuzzy neural network (FNN) is the main regulator controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the FNN controller. A tuning methodology is derived to update weight parts of the FNN. Using Lyapunov law, we derive the decoupled sliding-mode control law and the related parameters adaptive law of FNN. The method can control one-input and multi-output nonlinear systems efficiently. Using this approach, the response of system will converge faster than that of previous reports.
In this paper, we discuss a neural network based on hebbian learning rule for finding the inverse of a matrix. First we described finding the inverse of a matrix by mentioned neural network. Finally, experimental results for square and non-square matrices are presented to show the effectiveness of the approach. Proposed method is also scalable for finding the inversion of large-scale matrices.
In this correspondence paper, a theorem is given based on the main results of Kariwala et al. 1 for input-output pairing analysis for uncertain multivariable systems. A method to compute the relative gains' variation bound of RGA to inputoutput pairing analysis is provided. The results can decrease the computational load in large-scale uncertain systems, solve the sensitivity analysis problem, and propose the appropriate pair, when there is no sign change for relative gains.
In this study, a hybrid learning algorithm for training the recurrent fuzzy neural network (RFNN) is introduced. This learning algorithm aims to solve main problems of the gradient descent (GD) based methods for the optimization of the RFNNs, which are instability, local minima and the problem of generalization of trained network to the test data. PSO as a global optimizer is used to optimize the parameters of the membership functions and the GD algorithm is used to optimize the consequent part's parameters of RFNN. As PSO is a derivative free optimization technique, a simpler method for the train of RFNN is achieved. Also the results are compared to GD algorithm.
An improved design procedure for multi-input/multi-output (MIMO) quantitative feedback theory (QFT) problems involving tracking error specifications (TESs) has been presented. Appropriate transformation of the MIMO system to a series of equivalent single-input/single-output (SISO) problems is presented that motivates an improved synthesis procedure using feedback compensator and pre-filter transfer function matrices (TFMs). The key features of the procedure are that, for each equivalent SISO problem, (i) interactions and the effects of uncertainty are treated as an output disturbance, and (ii) sufficient conditions can be determined that assure desired levels of robust performance within the bandwidth region at a transformation cost that can be computed a priori. This paper also considers how the individual elements of the pre-filter TFM can be designed for MIMO QFT problems with a reduced level of conservatism and over-design using existing SISO methods. A benchmark quadruple-tank process is considered to illustrate the benefits of the new design paradigm.
Decentralized control is a well established approach to control the multivariable processes. In this approach, control structure design and in particular input-output pairing is a vital stage in the design procedure. There are several powerful methods to select the appropriate input-output pair in linear multivariable systems. However, despite the fact that most practical processes are nonlinear, there is no general method to select the appropriate input-output pair for nonlinear multivariable systems. In this study, a new general approach to input-output pairing for linear and nonlinear multivariable systems is proposed. Simulation results are employed to show the effectiveness of the proposed methodology.
This paper present power system load frequency control by modified dynamic neural networks controller. The controller has dynamic neurons in hidden layer and conventional neurons in other layers. For considering the sensitivity of power system model, the neural network emulator used to identify the model simultaneously with control process. To have validation of proposed structure of neural network controller the results of simulation demonstrated that the proposed controller offers better performance than conventional neural network controller.
Use of multi-objective particle swarm optimization for designing of planar multilayered electromagnetic absorbers and finding optimal Pareto front is described. The achieved Pareto presents optimal possible trade offs between thickness and reflection coefficient of absorbers. Particle swarm optimization method in comparison with most of optimization algorithms such as genetic algorithms is simple and fast. But the basic form of multi-objective particle swarm optimization may not obtain the best Pareto. We applied some modifications to make it more efficient in finding optimal Pareto front. Comparison with reported results in previous articles confirms the ability of this algorithm in finding better solutions.
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). This approach based on multi objective optimization mechanism for training parameters in antecedent part. It considers two cost functions as the objectives which are the maximum difference measurements between the real nonlinear system and the nonlinear model, and training mean square error (MSE). The NSGA-II is the multi objective optimization algorithm which employed for this purpose. So we use gradient decent (GD) method for training all parameters in conclusion part. Finally we show simulation results of applied this method to some nonlinear identification system.
In this paper, two methods of designing controller for a practical MIMO Flow-Level pilot plant are achieved. Since the open loop plant is unstable, it is stabilized by using inner controller. Then the stabilized plant is identified at different operating points. A nominal model which is non-minimum phase is selected. This system has two outputs which have completely different time constants. Therefore, the identification problem is difficult. The other problem associated with the system is the time delay that is one of the characteristics of the process plants. The transfer function of system is close to triangular and the RGA matrix of plant is close to the identity matrix. Hence, decentralized control can be a good selection for this system which is used in our work. The robustness of the system with decentralized controller is also checked. Two Hinfin, robust controllers based on the knowledge of the disturbance are derived and the results are compared to the results of the decentralized one. To have a good robust performance the mu-synthesis and DK-iteration approach is used.
In this paper, a nonlinear fuzzy identification approach based on genetic algorithm (GA) and Takagi-Sugeno (TS) fuzzy system is presented for fuzzy modeling of a multi-input, multi- output (MIMO) dynamical system. In this approach, GA is used for tuning the parameters of the membership functions of the antecedent parts of IF-THEN rules and Recursive Least- Squares (RLS) algorithm is employed for parameter estimation of the consequent linear sub- model parts of the TS fuzzy rules. The presented method is implemented on a simulated nonlinear MIMO distillation column. The results show that the presented method gives a more accurate model in comparison with the conventional TS fuzzy identification approach.
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with recursive least square (RLS) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from Genetic Algorithm (GA) method and using Adaptive Weighted for PSO. The simulation results show that in comparison with current gradient based training, the novel training can have a comparable adaptation to complex plants and train less parameter than gradient base methods. Also, the results show this new hybrid approach has less complexity than other gradient based methods.
Voltage stability may be improved by various control functions. In this paper, it is shown that how High Side Voltage Control (HIVC) may be employed for this purpose. Two test systems, namely a 22-bus and IEEE U8-bus systems are used to demonstrate the proposed tuning strategy for HSVC control parameters.
Decentralised control is widely used for the control of multivariable plants. Prior to the design of the decentralised controllers, input-output pairing is an important step in the design procedure. In the face of unknown, uncertain or time varying plant parameters, the input-output selection may endure fundamental changes, which will severely degrade the decentralised controller performance. This paper proposes a reconfigurable structure for the design of the decentralised controller based on the adaptive control strategies. Simulation results are provided to show the effectiveness of the proposed methodology.
Abstract Simulation of bending vibration effects on a two-stage launch vehicle and design of a new adaptive algorithm to reduce the flexible behaviours are discussed in this paper. The new adaptive algorithm uses recursive least square (RLS) method and two notch filters for decoupling rigid and flexible dynamic of the launch vehicle, estimating the bending frequency and reducing vibration effects. Applying this adaptive controller to the launch vehicle control system satisfied all design requirements. As the designed adaptive controller decouples rigid and flexible dynamics for estimating the bending frequency, it is simpler and faster than the other approaches and uses less CPU-capacity. The proposed approach validated by developing 6DoF nonlinear simulation software.
This paper presents sliding mode control of rotary inverted pendulum. Rotary inverted pendulum is a nonlinear, unstable and non-minimum-phase system. Designing sliding mode controller for such system is difficult in general. Here, first the desired performance is introduced and based on this performance two sliding surfaces are designed, then system is controlled by proper definition of a lyapunov function. The lyapunov function designed puts more emphasis on the control of the inverted pendulum rather than the control of the motor.
In this paper, a method for designing a stable fuzzy controller for nonlinear systems with the ability of reference tracking is introduced. First, the nonlinear equations of model helicopter is presented which is modeled by Humusoft Inc. based on linearization around system operating points, a TS fuzzy model is represented. Then, based on state feedback a TS fuzzy controller is developed, which its stability is guaranteed by nonlinear matrix Inequalities (LMI). These inequalities are solved using convex optimization methods by means of Matlab and LMI toolbox. In the following, tracking problem, control signal constraints are discussed.
This paper introduces a new approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO) with some modification in it to the training of all parameters of ANFIS structure. These modifications are inspired by natural evolutions. Finally the method is applied to the identification of nonlinear dynamical system and is compared with basic PSO and showed quite satisfactory results.
This study addresses new hybrid approaches for velocity control of an electro hydraulic servosystem (EHSS) in presence of flow nonlinearities and internal friction. In our new approaches, we combined classical method based-on sliding mode control and fuzzy RBF networks. The control by using adaptive networks need plant's Jacobean, but here this problem solved by sliding surface. It is demonstrated that this new technique have good ability control performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. The controllers which introduced have big range for control the system.
In this paper, a novel Two-Degree-Of-Freedom (2DOF) design procedure for Multi-Input Multi-Output Quantitative Feedback Theory (MIMO QFT) problems with Tracking Error Specifications (TESs) is presented. In the proposed procedure, the feedback compensator design is separated from the pre-filter design, using the model matching approach and the unstructured uncertainty modeling concept. This paper specially deals with an appropriate transformation of the MIMO system to the equivalent SISO problems, which allows easy design. Simulation results have been provided to show the effectiveness of the proposed methodology.
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with gradient decent (GD) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from genetic algorithm (GA) method. The simulation results show that in comparison with current GD training, the novel training can have a better adaptation to complex plants. Also, the results show this new hybrid approach optimizes ANFIS parameters faster and better parameters than gradient base method
Advanced high side voltage control (HSVC) regulation presents an attractive proposition for power system control. By proper tuning of its parameters, it can improve the voltage profile of the system. In this paper, we show how it can also enhance the loadability of a multimachine system. The genetic algorithm (GA) is employed to tune the parameters. Two test systems, a 21 bus and the IEEE 118 bus, are used to check the capability of the proposed algorithm.
This paper presents a neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a temperature control pilot plant. Such systems include highly nonlinear behavior and it is complicated to obtain an accurate physical model. Therefore, it is necessary to use such appropriate tools providing suitable models while preventing computational complexities. The identification results of pilot plant confirm the high performance of proposed method in two operational modes.
This paper introduces a new approach for routing in telecommunication networks. In this approach some theoretical foundations from mathematical modeling theory and integer programming have been exploited to develop a framework for routing problems. Some binary variables are assigned to the network links and for each link the corresponding binary variable shows the presence of the corresponding link on a specified route. The optimal route is determined in the source router per connection request by optimization of an objective function. An estimate of the residual bandwidth of network links is maintained in the source router. This information is used in the optimization problem to select the best available route from the source router to the destination router based on the selected metric. Required characteristics of a route are specified as logical constraints on the optimization variables. By using some tools from mathematical modeling theory, these logical constraints are transformed into equivalent integer linear inequalities. This technique results in a well-defined integer linear programming optimization problem
Recently, a great amount of interest has been shown in the field of modeling and control of hybrid systems. One of the efficient methods in this area utilizes the mixed logical-dynamical (MLD) systems in the modeling. In this method, the system constraints are transformed into mixed-integer inequalities by defining some logic statements. In this paper, a system containing three tanks is modeled as a nonlinear switched system using the MLD framework. Regarding this three-tank modeling, an n-tank system is modeled and number of binary and continuous auxiliary variables and also number of mixed-integer inequalities are obtained in terms of n. Then, the system size and complexity due to increase in number of tanks are considered. It is concluded that as the number of tanks increases, the system size and complexity increase exponentially which hampers control of the system. Therefore, methods should be found which result in fewer variables
Today satellites have important role in all parts of human living. For satellite correct communication should track satellite and so satellite antenna or camera should track Earth station correctly. Both correct attitude control and attitude determination are two factors to tracking from satellite. By using attitude control simulator could simulate attitude of satellite in each point of orbit with each disturbance. In this paper design of a satellite simulator by different methods of control and determination is investigated and some results are presented
This paper has introduced a new method for feature subset selection to which less attention has been given. Most of the past works have emphasized feature extraction and classification using classical methods for these works. The main goal in feature extraction is presented data in lower dimension. One of the popular methods in feature extraction is principle component analysis (PCA). This method and similar methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper we introduced particle swarm optimization (PSO) as a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. We used PCA for feature extraction and support vector machines (SVMs) for classification. The goal is to search the PCA space using PSO to select a subset of eigenvectors encoding important information about the target concept of interest. Another object in this paper is to increase speed of convergence by using PSO to find the best feature. We have tested the framework in mind on challenging application like face detection. Our results illustrate the significant improvement in this case
In this paper a novel hybrid strategy is employed in order to improve the controller performance. The main idea is combination of classical and intelligent controllers. Feedback error learning (FEL) as a two degrees of freedom (2DOF) control scheme, has been introduced based on this idea. This paper takes a step ahead of traditional FEL schemes which combine a PID controller with an intelligent inverse based controller. We introduce a robust FEL scheme and the robust controller replaces the conventional PID controller. The Robust controller is designed based on the Hinfin approach and the intelligent controller has ANFIS structure. This novel algorithm is implemented in a Flow plant to track the desired value of flow and reject unwanted disturbances in the practical system. The results are brought to prove the practical power of the novel method and are compared with other control schemes.
Decentralized control is a well established approach to the control of multivariable plants. In this approach, control structure design and in particular input-output pairing is a vital stage in the design procedure. There are several methods such as RGA, balanced-realization, Hankel-norm based and gramian based approach to select the appropriate input/output pairs in linear multivariable plants. In this paper, a new input-output pairing method for stable multivariable plants is proposed. This new approach is based upon the cross-gramian matrix of SISO elementary subsystems built from the original MIMO plant. The main advantages of the method are simplicity and proposing an overall measure to choose the best input output pairs
This paper discusses application of an intelligent system in order to navigate in real-time a small size, four wheeled, indoor mobile robot accurately using ultra-light (160 gr), inexpensive laser range finder without prior information of the environment. A recurrent neural network is used to find the best path to the target of the robot. An accurate grid-based map is generated using a laser range finder scene and location found by a modified dead reckoning system. Finally a motion control method is presented. These approaches are implemented and tested in Resquake mobile robot
Recently, a great amount of interest has been shown in the field of modeling and controlling hybrid systems. One of the efficient and common methods in this area utilizes the mixed logicaldynamical (MLD) systems in the modeling. In this method, the system constraints are transformed into mixed-integer inequalities by defining some logic statements. In this paper, a system containing three tanks is modeled as a nonlinear switched system by using the MLD framework. Then, regarding this three-tank modeling, an ntank system is modeled and number of binary and continuous auxiliary variables and also number of mixed-integer inequalities are obtained in terms of n. Thereafter, the system size and complexity due to increase in number of tanks are considered. It is concluded that as number of tanks increases, the system size and complexity increase exponentially which hampers control of the system. Thus, it seems necessary to find some appropriate techniques for decreasing number of variables.
Background: The aim of this study was to investigate the concept of 'Nonaffective Acute Remitting Psychosis' (NARP) in a group of first episode psychotic patients admitted to a psychiatric hospital in Tehran, Iran.Materials and Methods: The data are from a 24-month follow-up study of 54 first-episode non-organic psychotic patients admitted consecutively to an acute care academic hospital in Tehran, Iran. Patients were followed for two years at the time of discharge, as well as 3, 6, 12, 18, and 24-month intervals. At the end of follow-up, consensus judgments were made on the fulfillment of the NARP criteria as well as the course of illness and treatment. NARP was defined as a psychotic illness with acute onset (developed within 1 week), short duration (remission within 6 months since the onset), and the absence of prominent mood symptoms.Results: Five patients were lost to follow-up. Of remaining 49, 15 patients had NARP (9 women, 6 men) that constituted 30.6% of the sample and accounted for 60% of the patients with non-affective psychosis. Of these, 10 remained relapse free throughout the 24-month follow-up, four had a very short-lived relapse, and only one patient developed a chronic illness. Duration of the index episodes was under three months in all cases. In the course of follow-up, the patients with NARP received less months of treatment than did the patients with other non-affective psychoses.Conclusion: The strikingly high proportion of NARP diagnosis among patients with first episode psychosis, and the favorable course is in keeping with previous studies in developing countries.
A gradient based approach for the design of set-point tracking adaptive controllers for nonlinear chaotic systems is presented. In this approach, Lyapunov exponents are used to select the controller gain. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, an effective adaptive strategy is used for online identification of Lyapunov exponents and adaptive control of nonlinear chaotic plants. Also, a nonlinear observer for estimation of the states is proposed. Simulation results are provided to show the effectiveness of the proposed methodology.
Decentralized Control is a well established approach to the control of multivariable plants. In this method, control structure design and in particular inputoutput selection is a vital stage in the design procedure. There are several powerful methods to select the appropriate input/output pairs in linear multivariable plants. However, there is no general procedure to select the appropriate input/output pairs for nonlinear multivariable plants and linear multivariable plants in the presence of uncertainties, despite the fact that most practical systems are nonlinear and uncertain. In this paper, a new on-line estimation for RGA matrix using neural network for nonlinear or uncertain linear multivariable plants is proposed.
Quantitative Feedback Theory (QFT) is one of most effective methods of robust controller design and can be considered as a suitable method for systems with parametric uncertainties. Particularly it allows us to obtain controllers less conservative than other methods like H∞ and µ-synthesis. In QFT method, we transform all the uncertainties and desired specifications to some boundaries in Nichols chart and then we have to find the nominal loop transfer function such that satisfies the boundaries and has the minimum high frequency gain. The major drawback of the QFT method is that there is no effective and useful method for finding this nominal loop transfer function. The usual approach to this problem involves loop-shaping in the Nichols chart by manipulating poles and zeros of the nominal loop transfer function. This process now aided by recently developed computer aided design tools proceeds by trial and error and its success often depends heavily on the experience of the loop-shaper. Thus for the novice and First time QFT user, there is a genuine need for an automatic loopshaping tool to generate a first-cut solution. In this paper, we approach the automatic QFT loop-shaping problem by using a procedure involving Linear Programming (LP) techniques and Genetic Algorithm (GA).
In this paper, we will present population based method for placement of center of radial basis function of a locally linear neuro-fuzzy (LLNF) network, which is trained by LOLIMOT algorithm. Originally, LOLIMOT algorithm incrementally divides the hyper-rectangles on input space into two axes orthogonal directions in half. However, this heuristic method would not be the best possible partitioning of the space. We present and evaluate a new particle swarm optimization (PSO) method for finding the best divisions of input space in the LOLIMOT algorithm
This paper discusses the design of a cascade controller for active suspension systems, to improve ride quality. In order to do this, in the main loop, a model reference adaptive controller is designed to attenuate disturbances due to rough roads. An internal loop provides the required control force for the main controller. The closed loop system has desired robust stability and performance in the presence of uncertainty due to time varying parameters and nonlinear dynamics of the actuator. The simulation results show the effectiveness of the suggested method in increasing ride comfort and safety while constrains of suspension system maneuverability is also satisfied
Hybrid systems theory as a growing field in control theory provides some contributions for traditional control problems. Control of switched linear systems as a member of these categories has well-known solutions like multiple model control. Multiple model controllers provide a global control action by interpolating the individually-designed controllers. Since the hybrid systems methods are usually more complicated than the conventional control schemes, it is of great importance to explain these potential superiorities. The goal of this paper is to explain potential benefits which could be achieved by using hybrid control methods. Predictive control - as a powerful strategy to deal with complicated dynamics - is selected as the design basis for hybrid controller and for a multiple model controller. An illustrative test bench problem is introduced to compare the behavior of two controllers. It has been shown that the hybrid controller provides more intelligent and systematic design procedure for control of switched linear systems and is superior to multiple model controller in the sense of speed of response, optimality and domain of attraction
In this paper, generalized predictive control (GPC) algorithm is implemented to control an earth station antenna. Nonlinear term in motors caused by gearbox or other parts is modeled by a backlash block. Simulation results show the effectiveness of GPC method for robust control in the presence of backlash nonlinearity without a priori knowledge about upper and lower bounds of backlash. Also, adaptation mechanism as a self tuning predictive control is used to conquer environment changing
In this paper, generalized predictive control (GPC) algorithm is implemented to control an Earth station antenna. Nonlinear term in motors caused by coulomb friction is modeled by a dead zone block. Simulation results show the effectiveness of GPC method for robust control in the presence of dead zone nonlinearity without a priori knowledge about upper and lower bounds of dead zone
In this paper, generalized predictive control (GPC) algorithm is implemented to control an Earth station antenna with a non-minimum phase motor. Nonlinear term in motors caused by gearbox or other parts is modeled by a backlash block. Simulation results show the effectiveness of GPC method for robust control in the presence of backlash nonlinearity without a priori knowledge about upper and lower bounds of backlash. Also, adaptation mechanism as a self tuning predictive control is used to conquer environment changing
In this paper, we propose a one-layered neural network that recovers its input variables by genetic algorithms to solve the systems of linear equations (or, equivalently, matrix inversion). First we described solving systems of linear equations (matrix inversion) by mentioned neural network. Then, experimental results are presented to show the effectiveness of the approach. Finally, future avenue of this research is proposed.
Current state-of-the-art approaches for control of hybrid systems face with two main important challenging problems which are guaranteeing the stability and the computational complexity. In this article a new approach has been proposed to guarantee the closed loop stability of a class of hybrid systems while reducing the complexity of control problem by introducing some level of suboptimality. It has been shown that using contraction constraint on the objective function results in asymptotically stable closed loop system. It has also been described that since only feasibility is sufficient for stability in the proposed approach, suboptimal control could be used to reduce the computational complexity.
This paper describes the implementation of picture stabilizer in 3-degree of freedom table in image stabilizer. There are two innovative aspects of this work. First, parameter estimation is used to adapt the feedforward compensation terms instead of the gains of the feedback controller, as usually is the case in conventional indirect self-tuning regulators. Second, the complete adaptive controller has been implemented with C program and PCL812 card and encoder card and motor driver for command the motors. In result one method with hybrid increase accuracy system, specially when input error signal is large and need to maximum speed control system. In this system frequency of 0.3 Hz , thus we use gyro for estimate of table position. In this paper, it is specifically implemented and demonstrated on a gyro mirror line-of sight (LOS) system
In this paper we propose a new data fusion method based on particle filtering and fuzzy logic in order to adaptively integrate global positioning system and strapdown inertial navigation system (GPS/SDINS). This approach will reduce the dependence of the stable solution on stochastic properties of the system which is a function of vehicle dynamics and environmental conditions So the proposed scheme will enhance the estimation performance in comparison with generic particle filter specially in the case of facing modeling uncertainty. It will also give us more reliable solution when encountering satellite signal blockage as a probable problem in land navigation. The results have clearly demonstrated that the hybrid fuzzy particle filter would improve the guidance from the point of accuracy and robustness to the mentioned problems.
A model-based approach to adaptive control of chaos in non-linear chaotic discrete time systems is presented. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, an effective adaptive strategy is proposed for on-line identification of Lyapunov exponents. The control aim is that the plant output changes in accordance with the output of the linear desired model. Simulation results are provided to show the effectiveness of the proposed methodology.
A new approach to adaptive control of chaos in non-linear discrete time systems with delayed state feedback is presented. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, Lyapunov exponents are used to select the controller gain. An effective adaptive strategy for on-line identification of Lyapunov exponents is proposed. Simulation results are provided to show the effectiveness of the proposed methodology.
One of the most important problems of delayed and nonlinear systems is to fulfill multiple goals simultaneously and in the best conditions. This paper presents a method for controlling systems with multiple goals. The method is based on context and has a neuro-fuzzy structure with capability of temporal difference learning. The proposed method, regarding the current status, prior system conditions, and current control goals, would be capable of controlling the system in a way that these goals are achieved in the best way and the least time. In order to clarify the issue and prove the capabilities of the proposed method two well-known control problems, achieving the multiple goals of which, in the best way and the least time, would be very difficult through manipulating other control methods, would be faced using the proposed method.
In the past decade, because of wide applications of hybrid systems, many researchers have considered modeling and control of these systems. Since switching systems constitute an important class of hybrid systems, in this paper a method for optimal control of linear switching systems is described. The method is also applied on the two-tank system which is a much appropriate system to analyze different modeling and control techniques of hybrid systems. Simulation results show that, in this method, the goals of control and also problem constraints can be satisfied by an appropriate selection of cost function.
Mixed logical dynamical (MLD) modeling appears as an effective and realistic approach in modeling and control of hybrid systems. In this modeling approach, dynamical and logical constraints as well as control system design specifications are transformed into so-called mixed-integer inequalities. In this paper, the MLD framework is used for modeling of a multi-tank system as a switched nonlinear system. Control of fluid levels in multiple tanks is considered as a case study for predictive control of MLD systems. Translation of control problem specifications into mixed-integer inequalities shows the ability of MLD framework to deal with complex modeling and optimization tasks
In this paper, different approaches to solve the forward kinematics of a three DOF actuator redundant hydraulic parallel manipulator are presented. On the contrary to series manipulators, the forward kinematic map of parallel manipulators involves highly coupled nonlinear equations, which are almost impossible to solve analytically. The proposed methods are using neural networks identification with different structures to solve the problem. The accuracy of the results of each method is analyzed in detail and the advantages and the disadvantages of them in computing the forward kinematic map of the given mechanism is discussed in detail. It is concluded that ANFIS presents the best performance compared to MLP, RBF and PNN networks in this particular application.
Quantitative design of robust control systems proposes a transparent and practical controller design methodology for uncertain plants. In the case of large plant uncertainties, the resulted robust controller would be unnecessarily high order with large bandwidth. On the other hand, adaptive controllers can also tackle the control of unknown uncertain plants. However, the controller would be nonlinear and time varying. In this paper, a combined design methodology based on quantitative feedback theory (QFT) and externally excited adaptive system (EEAS) is proposed. This controller can handle large plant parameter uncertainties with lower bandwidth. Also, a random optimization technique is employed to optimally design the overall robust adaptive controller. Simulation results are used to show the effectiveness of the proposed design methodology.
Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with respect to the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS integration in comparison with conventional ANFIS specially in the cases of satellites’ outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.
This paper proposes a reconfigurable controller design method for multivariable systems, which is capable of dealing with order-change problems that may occur in an after-fault system. A new method is proposed to recover the nominal closed-loop performance after a fault occurrence in the system. This approach uses the eigenstructure assignment. Unlike the previously developed approaches, the new method can be implemented in the case when the fault leads to order change of the after-fault model. Also, it can be used to solve the problems in which the set of after-fault open-loop and closed-loop eigenvalues have common elements, especially when the system becomes uncontrollable or unobservable due to the fault. The method guarantees the stability of the reconfigured closed-loop system in the presence of output feedback. Finally, simulation results are provided to show the effectiveness of the proposed method for an aircraft model.
This paper presents a novel approach to solve the MIMO-QFT problem for tracking error specification through a method of obtaining exact bounds for the design of individual elements of pre-filter. The paper specifically deals with the appropriate transformation of the MIMO system to the equivalent SISO problems, which allows easy design to find the feedback compensator and pre-filter. A linearized model of quadruple-tank process is used to show the effectiveness of the proposed method.
New approaches to design static and dynamical reconfigurable control systems are proposed based on the eigenstructure assignment techniques. The methods can recover the nominal closed-loop performance after a fault occurrence in the system, in the state and output feedback designs. These methods are capable of dealing with order-reduction problems that may occur in an after-fault system. The problem of robust reconfigurable controller design, which makes the after-fault closed-loop system insensitive as much as possible, to the parameter uncertainties of the after-fault model is considered. Steady state response of the after-fault system under the unit step input is recovered by the means of a reconfigurable feed-forward compensator. The methods guarantee the stability of the reconfigured closed-loop system in the case of output feedback. For the faulty situations, in which the order of the pre-fault and after-fault closed-loop systems are the same, sufficient regional pole assignment conditions for the reconfigured system are derived. Finally, simulation results are provided to show the effectiveness of the proposed methods for two aircraft models.
This paper proposes a reconfigurable control system design methodology using the sliding-mode control. The advantage of the proposed sliding-mode reconfigurable control methodology is that it is more robust than the simple static reconfigurable feedback. An approach is suggested to redesign the sliding surface for the after-fault variable structure controller using the genetic algorithms. So, the new sliding-mode controller is capable of preserving much of the dynamics of the original unfailed system. Simulation results are provided to show the effectiveness of the proposed method.
Time series processes can be classified to three models, linear models, stochastic models and chaotic models. Based on these classification the linear models are forecastable, the stochastic models are unforecastable and the chaotic models are semi forecastaable. The previouse researches in the modeling and forecasting of the stock price usually try to prove that, the fluctuations of the share prices in Tehran Stock Exchange are not random walks in spite of the existance similarity to the random walks. Indeed the market has a chaotic behavior. This means that, the Efficient Market Hypothesis (EMH) is failed. Therefore by using a complex and powerfull models such as artificial neural networks, one can forecast stock prices in tehran stock merket. This paper proposed another approach to modeling and forecasting of the share price. This approach is based on the Stochastic Differential Equations. The modeling is based on the Black- Scholes pricing model. Comparison the simulation result with the linear ARIMA model, indicates that the proposed structrure, provides an accurate next step and the long term share prices and daily returns forecasting.
One of the most important problems which affect the performance of active suspension system is variation in suspension parameters such as tire stiffness, mass of body, etc. In this paper two robust approaches are applied to active suspension: H/sub /spl infin// and QFT. The performance of these two controllers is examined in the presence of parameter variation and actuator nonlinear dynamics. Simulation results show that QFT is effective in the robust control of active suspensions in automobiles and so it is useful for automobile engineers to think about using this algorithm in automobiles.
This paper considers the adaptive computation of Lyapunov Exponents (LEs) from time series observations based on the Jacobian approach. It is shown that the LEs can be calculated adaptively in the face of parameter variations of the dynamical system. This is achieved by formulating the regression vector properly and adaptively updating the parameter vector using the Recursive Least-Squares principles. In cases where the structure of the dynamical system is unknown, a general non-linear regression vector for local model fitting based on a locally adaptive algorithm is presented. In this case, the Recursive Least-Squares method is used to fit a suitable local model, then by state space realization in canonical form, the Jacobian matrices are computed which are used in the QR factorization method to calculate the LEs. This method essentially relies on recursive model estimation based on output data. Hence, this on-line dynamical modeling of the process will circumvent the computations typically required in the reconstructed state space. Therefore, difficulties such as the problem of large number of data and high computational effort and time are avoided. Finally, simulation results are presented for some well-known and practical chaotic systems with time varying parameters to show the effectiveness of the proposed adaptive methodology.
Pairing is an important task in controlling of a MIMO plant by some SISO sub- control systems. Using balance realization is one of the methods to find the best pairing. In this article, an algorithm is proposed to find an optimal controllable and observable input- output pairing. The algorithm can utilize when the plant parameters change and propose the new pairing to adapt the control structure.
One of the most important issues that we face in controlling delayed systems and non-minimum phase systems is to fulfill objective orientations simultaneously and in the best way possible. In this paper proposing a new method, an objective orientation is presented for controlling multi-objective systems. The principles of this method is based an emotional temporal difference learning, and has a neuro-fuzzy structure. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attain in the least amount of time and the best way. To clarify the issue and verify the proposed the method, three well known control examples which are hard to handle through classic methods are handled by means of the proposed method.
In this paper an H∞ controller is designed for a hydraulically actuated active suspension system of a half-modeled vehicle in a cascade feedback structure. Using the proposed structure the nonlinear behavior of actuator is reduced significantly. In the controller synthesis, a proportional controller is used in the inner loop, and a robust H∞ controller forms the outer loop. Two H∞ controllers are designed for this system. First unstructured uncertainty is not considered in the design procedure and secondly, the controller is designed considering uncertainty. Each of these controllers is designed in a decentralized fashion and the vehicle oscillation in the human sensitivity frequency range is reduced to a minimum. Statistical analysis of the simulation result using random input as road roughness, illustrates the effectiveness of the proposed control algorithm for both cases.
We investigate tracking filters in electro-optical target-tracking systems with bearing-only measurements and a stationary tracker. In passive tracking, for maintaining the target in the camera field of view, two tracking angles should be controlled. To extract the target position, there is at least one frame period latency resulting from time duration required for image processing. Three filtering methods, a simple Kalman filter, a novel filtering approach based on curve fitting on time series data, and an interactive multiple model filter, are studied. Since target range is neither available nor observable, in the all mentioned techniques, instead of applying filters to the target states (position and velocity in the space), each filter is directly applied to the tracking angles. The performance of each filter in this approach is evaluated by tracking angles error with two maneuvering targets.
In this paper, a method for estimating an attractor embedding dimension based on polynomial models and its application in investigating the dimension of Bremen climatic dynamics are presented. The attractor embedding dimension provides the primary knowledge for analyzing the invariant characteristics of the attractor and determines the number of necessary variables to model the dynamics. Therefore, the optimality of this dimension has an important role in computational efforts, analysis of the Lyapunov exponents, and efficiency of modeling and prediction. The smoothness property of the reconstructed map implies that, there is no self-intersection in the reconstructed attractor. The method of this paper relies on testing this property by locally fitting a general polynomial autoregressive model to the given data and evaluating the normalized one step ahead prediction error. The corresponding algorithms are developed in uni/multivariate form and some probable advantages of using information from other time series are discussed. The effectiveness of the proposed method is shown by simulation results of its application to some well-known chaotic benchmark systems. Finally, the proposed methodology is applied to two major dynamic components of the climate data of the Bremen city to estimate the related minimum attractor embedding dimension.
In this paper an objective orientation is presented for controlling multi-objective systems. The principles of this method is based on an emotional learning and temporal difference learning, and has a neuro-fuzzy structure. The proposal method can control the system in a way that objectives such as the present conditions, the system action in the part and the controlling aims are attained in the best way and least amount of time.The holistic structure of this paper is as follows: first, in the third unit object oriented control is studied. The fourth unit deals with emotional learning as a method for intelligent control. In the fifth unit the emotional control of temporal difference is defined and discussed. The sixth unit presents and discusses a new critic structure with temporal difference learning and then Some Multivariable systems, which are one of the most important controlling problems because of coupling, are defined. Then in the last unit the functional control structures used in this paper are studied and the experimental results are compared.
This paper presents a modified method for approximating systems by a sequence of linear time varying systems. The convergence proof is outlined and the potential of this methodology is discussed. Simulation results are used to show the effectiveness of the proposed method.
Genetic algorithms for optimization of the multiple model and variable structure estimators are discussed in this paper. The estimation algorithm based on the multiple model and variable structure, are the best approach used in many systems, including maneuvering target tracking, noise recognition, etc. The RAMS algorithm, asserts that a multiple model algorithm consists of three steps: model set adaptation, initialization of model-based filters, and estimation. The first step, i.e., model set adaptation, is unique for VSMM algorithm and is the only superiority of the VSMM over FSMM. After the graph theory is used for this step and the sub-optimal switching digraph algorithm is discussed, we try to use the genetic algorithm for optimizing the thresholds used in the sub-optimal algorithm. The simulations show the improvement of the system performance when we use the optimal variable structure multiple model approach.
This paper proposes a reconfigurable controller design method in the case that full state feedback is not permissible. A new sufficient condition to guarantee the stability of output feedback reconfigurable controller is suggested. Based on the new condition, an algorithm is introduced that preserves much of the dynamics of the original unfailed system using eigenstructure assignment and genetic algorithms. The new algorithm guarantees the closed-loop stability of the reconfigured system.
The existing methods of decentralized control suffer from two major restrictions. First, almost all of them hinge on Lyapunov's method, and second, they do not address the problem of performance robustness. A novel methodology to overcome the above defects is presented in this paper. Central to this approach is the notion of a finite-spectrum-equivalent descriptor system in the input-output decentralized form. By way of this notion, a new formulation of the interaction which introduces some degrees of freedom into the design procedure is offered. The main result, i.e. a sufficient condition for decentralized performance stabilization in a desirable performance region and maximal robustness to unstructured uncertainties in the controller and plant parameters, nevertheless, is in terms of regular systems. Based on minimal sensitivity design of isolated subsystems via eigenstructure assignment, an analytic method for the satisfaction of the aforementioned sufficient condition is also presented.
In this paper a new method for decentralized stabilization of a large-scale system in general form via state-feedback is presented. An appropriate descriptor system is defined for a large-scale system, such that the new system is in input-decentralized form. The interactions between the subsystems are considered as uncertainty. Sufficient conditions for stability of the closed-loop uncertain system are introduced. By appropriately assigning the eigenstructure of each isolated subsystem, these conditions are satisfied. This is accomplished by using the method suggested by Patton and Liu, such that the effects of the interconnections between the subsystems are compensated via the combination of genetic algorithms and gradient-based optimization.
The problem of Lyapunov Exponents (LEs) estimation from chaotic data based on Jacobian approach by polynomial models is considered. The optimum embedding dimension of reconstructed attractor is interpreted as suitable order of model. Therefore, based on global polynomial mode ling of system, a novel criterion for selecting the embedding dimension is presented. By considering this dimension as the model order, the best nonlinearity degree of polynomial is estimated. The selected structure is used for local estimating of Jacobians to calculate the LEs. This suitable structure of polynomial model leads to better results and avoids of sporious LEs. Simulation results show the effectiveness of proposed methodology.
In this paper, the problem of Lyapunov Exponents (LEs) computation from chaotic time series based on Jacobian approach by using polynomial modelling is considered. The embedding dimension which is an important reconstruction parameter, is interpreted as the most suitable order of model. Based on a global polynomial model fitting to the given data, a novel criterion for selecting the suitable embedding dimension is presented. By considering this dimension as the model order, by evaluating the prediction error of different models, the best nonlinearity degree of polynomial model is estimated. This selected structure is used in each point of the reconstructed state space to model the system dynamics locally and calculate the Jacobian matrices which are used in QR factorization method in the LEs estimation. This procedure is also applied to multivariate time series to include information from other time series and resolve probable shortcoming of the univariate case. Finally, simulation results are presented for some well-known chaotic systems to show the effectiveness of the proposed methodology.
In this paper, we deal with several time series of daily share prices and daily returns of different companies which are members of the Tehran Stock Exchange. Three forecastability methods as nonlinear mathematical analysis were applied to the data obtained for daily share prices and daily returns in Tehran Stock Exchange during three and half years. The characteristics of the process associated with these time series were analyzed. Analyzing the behavior of the time series associated with these companies is indicative of their short-term predictability nature. However, using analysis regarding the correlation dimension estimate, indicated that only the time series information are not adequate for prediction and other appropriate variables must also be used. Also, using a Rescaled-range analysis, showed that past information have long term effects on the market and are useful in the process of prediction. Also, the analysis of the Largest Lyapunov Exponent estimate revealed a weakly chaotic behavior and indicated that time series data cannot be used in the prediction process after a certain time. It is shown the time series generator process of these companies are complex nonlinear mappings and the methods based on the various linear modeling strategies are unable to identify these dynamics.
Among the search engines, Google is one of the most powerful. It uses an accurate ranking algorithm to order web pages in search results. In this paper, it is shown that a simple linear model can approximately model the dynamics overning the behavior of Google. Least Squares is used for the system identification procedure. Identification results are provided to show the effectiveness of the identified system.
The problem of embedding dimension estimation from chaotic time series based on polynomial models is considered. The optimality of embedding dimension has an important role in computational efforts, Lyapunov exponents analysis, and efficiency of prediction. The method of this paper is based on the fact that the reconstructed dynamics of an attractor should be a smooth map, i.e. with no self intersection in the reconstructed attractor. To check this property, a local general polynomial autoregressive model is fitted to the given data and a canonical state space realization is considered. Then, the normalized one step ahead prediction error for different orders and various degrees of nonlinearity in polynomials is evaluated. This procedure is also extended to a multivariate form to include information from other time series and resolve the shortcomings of the univariate case. Besides the estimation of the embedding dimension, a predictive model is obtained which can be used for prediction and estimation of the Lyapunov exponents. To show the effectiveness of the proposed method, simulation results are provided which present its application to some well-known chaotic benchmark systems.
This paper presents a modified method for approximating nonlinear systems by a sequence of linear time varying systems. The convergence proof is outlined and the potential of this methodology is discussed. Simulation results are used to show the effectiveness of the proposed method.
Because of increasingly application of fuzzy systems, great deal of attention has been paid to design of fuzzy controller system. Many methods of fuzzy controller design have been suggested such as genetic algorithm and neural network. In this paper, our purpose is to design fuzzy controller rule base by GA. In fact, by GA through possible rule base we search for a subset of optimal rules for fuzzy control of ball and plate system. Design of this controller is based on linear form of ball and plate system with the aid of Simulink and finally the result is implemented on plant.
In this paper, a mixed H/sub 2//H/sub /spl infin// controller is designed for 1/4 car suspension model. Neither H/sub 2/ nor H/sub /spl infin// controllers can provide goals of active suspension separately (i.e. minimizing body vertical acceleration considering restriction on suspension displacement). So, in this paper both objectives are considered in a mixed H/sub 2//H/sub /spl infin// problem. The Riccati equations are solved using a recursive algorithm and the mixed H/sub 2//H/sub /spl infin// controller performance is compared with H/sub 2/ and H/sub /spl infin// controllers through frequency and time domain simulations.
The input-output pairing of multivariable plants with parametric uncertainty can vary in the face of large plant parameter variations. The Relative Gain Array (RGA) analysis is a powerful tool for the input-output pairing of linear multivariable plants. In the case of parametric uncertainties, RGA elements may vary accordingly. Hence, a test is proposed to identify the change in the input-output pairing in the presence of parametric uncertainties.
In this paper, a method for design of linear decentralized robust controllers for a class of uncertain large-scale systems in general form is presented. For a given large-scale system, an equivalent descriptor system in input–output decentralized form is defined. Using this representation, closed-loop diagonal dominance sufficient conditions are derived. It is shown that by appropriately minimizing the weighted sensitivity function of each isolated subsystem, these conditions are achieved. Solving the appropriately defined H∞ local problem for each isolated uncertain subsystem, the interactions between the subsystems are reduced, and the overall stability and robust performance are achieved in spite of uncertainties. The designs are illustrated by a practical example.
In this paper, the problem of achieving robust stability for linear large-scale systems by decentralized feedback is considered. Sufficient conditions for stability of closed-loop system are introduced. By appropriately assigning the eigenstructure of each isolated subsystem via output feedback or state feedback, these conditions are satisfied. Based on the eigenstructure assignment result and the matrix eigenvalue sensitivity theory, a method for decentralized robust stabilization is presented.
This paper presents two new approaches for robust step tracking in structure uncertain nonlinear systems. The problem is first restated as a non linear optimal control infinite horizon problem, then with a suitable change of variable, the time interval is transfer to the finite horizon (0.1) this change of variable, poses a time varying problem. This problem is then transfer to measure space, and it is shown that an optimal measure must be determined which is equivalent to a linear programming problem with infinite dimension. Then, using finite horizon approximations, the optimal control law is determined as a piece wise constant function. Simulations are provided to show the effectiveness of the proposed methodology
Quantitative Feedback Theory (QFT) is one of the most effective methods of robust controller design. In QFT design, we can consider the phase information of the perturbed plant so it is less conservative than H∞ and µ-synthesis methods. In this paper, we want to overcome the major drawback of QFT method, i.e., lack of an automated technique for loop-shaping. Clearly such an automatic process must involve some sort of optimization, and while recent results on convex optimization have found fruitful applications in other areas of control theory we have tried to use LMI theory for automating the loop-shaping step of QFT design.
This paper considers the problem of achieving stability and certain performance for a large-scale system by a decentralised control feedback law. For a given large-scale system an equivalent descriptor system in input-output decentralised form is defined. For solving the performance problem which is formulated as the standard weighted mixed sensitivity H∞problem, modification of the original weighting functions is proposed. Some sufficient conditions are proposed when satisfied the overall stability and performance of the large-scale system is guaranteed.
This paper presents a new approach for solving of time optimal control in nonlinear problem using measure theory. This problem is transfer to measure space, and it is shown that an optimal measure must be determined which is equivalent to a linear programming problem with infinite dimension. Afterward, by suitable approximation it changes to a finite-dimensional linear programming. By solving the L.P. problems, optimal control function can determine such as a piecewise constant function.
In this paper a new method for robust decentralised control of large-scale systems using quantitative feedback theory (QFT) is suggested. For a given large-scale system an equivalent descriptor system is defined. Using this representation, closed-loop diagonal dominance sufficient conditions over the uncertainty space are derived. It is shown by appropriately choosing output disturbance rejection model in designing QFT controller for each isolated subsystem, these conditions are achieved. Then a single-loop quantitative feedback design scheme is applied to solve the resulting series of individual loops to guarantee the satisfaction of predefined MIMO quantitative specifications.
Necessary and sufficient conditions for minimum sensitivity (highest robustness) to unstructured uncertainty in linear output feedback design are presented. The approach is analytical and simple, and the solution is explicit, in compact form, and restriction-free. Genetic algorithm is employed to implement the proposed method.
Multiple modeling identification using the selforganizing map neural network has been introduced by authors . Two variations of that have been presented; MMSOM and MMISOM. MMSOM is based on using ordinary SOM and MMISOM utilizes the irregular SOM. In MMISOM, the neighborhood between the nodes may change. Therefore, MMISOM has more flexibility to cover concave spaces while SOM is more suitable for convex spaces. In this paper, after a review of both algorithms, some of the properties of MMISOM on the presence of noise are discussed.
This paper considers the problem of achieving stability and desired dynamical transient behavior for linear large-scale systems, by decentralized control. It can be done by making the effects of the interconnections between the subsystems arbitrarily small. Sufficient conditions for stability and diagonal dominance of the closed-loop system are introduced. These conditions are in terms of decentralized subsystems and directly make a constructive H∞ control design possible. A mixed H∞ pole region placement is suggested, such that by assigning the closed-loop eigenvalues of the isolated subsystems appropriately, the eigenvalues of the overall closed-loop system are assigned in desirable range. The designs are illustrated by an example.
In this article a high-gain decentralized controller is designed for a large-scale system. The effects of the interactions between the subsystems are cosidered as uncertainty for the large-scale system. A bound on the high-gain factor is computed to nullify the effects of the interactions and also to ensure the overall closed-loop stability. In order to avoid saturation, the anti-windup integrator method is used in designing high-gain controller. Due to high-gain feedback, the closed-loop system is robust with respect to outputdisturbances and uncertainties.
New necessary and sufficient conditions for multivariable pole placement (MVPP) and entire eigenstructure assignment (EEA) through static linear multivariable output feedback are established. It is shown that the resultant matrix is of full rank and all design freedoms are retained. The problem of static linear multivariable output feedback control law design is then defined. Based on the EEA concept and sufficiency of the regional pole placement, the design is (re)formulated in terms of a constrained nonlinear optimization problem. To this end, some decoupling indices for noninteractive performance are defined, their necessary and sufficient conditions are derived and tracker design is addressed. The problem formulation well suits the application of random/intelligent optimization techniques. By way of this approach, optimal robust stability/performance, noninteractive performance, reliability, actuator limitations and low sensitivity in the face of structured or unstructured plant uncertainties are achieved. The effectiveness of the proposed methodology is demonstrated by simulation results using genetic algorithm.
This paper presents the application of neural networks for the adaptive leveling and gyrocompassing of stable platforms. The stable platform is a three input and two output nonlinear plant, and the control of its error dynamics (leveling) is of vital importance for the proper operation of the inertial navigation systems of aircraft. Also, another important preflight step in the inertial navigation system using the stable platform is gyrocompassing. Gyrocompassing provides the navigation system with the wander angle, which is the angle between the Y-axis of the stable platform and true north.In this paper, neural networks are employed to identify the dynamics of the platform and to level it, based on the identified neural model; gyrocompassing is also performed using an inverse neural identification of the stable platform. In order to show the effectiveness of the proposed neural adaptive controller for platform leveling and gyrocompassing, the results of practical leveling tests performed on an inertial navigation unit of a fighter aircraft and simulation results for gyrocompassing are presented.
It has been previously shown that the dynamics governing the share prices in Tehran Stock Exchange can be considered as a chaotic time series. Due to the initial sensitivity of the price generating process, it is shown that linear classical models such as ARIMA and ARCH are not able to efficiently model the dynamic of share prices in Tehran stock exchange for long term prediction purposes. However, non-linear neural network models are proposed to model the Tehran price index (TEPIX) daily data process and it is shown that such nonlinear models can successfully be used for the long term prediction of TEPIX daily data. Real data for the period of 1996 to 1999 are used to validate the prediction results.
The problem of achieving stability and certain H/sub /spl infin// performance objective for a large-scale system by a decentralized feedback law is considered. It is shown in order to reduce the sensitivity to the interactions, the states of the other subsystems can be considered as external disturbances for each subsystem. An appropriate H/sub /spl infin// controller is designed for each subsystem. Solving H/sub /spl infin// problems for the subsystems, the sensitivity to the interactions is reduced and the performance problem which is formulated as the standard weighted mixed sensitivity H/sub /spl infin// problem, is solved. Sufficient conditions are derived when satisfied to assure the overall stability.
The multivariable linear output feedback technique isrecast as a constrained nonlinear optimization problem. An evolutionary, multiple-objective enetic algorithm is applied to encapsulate and globally optimallyreconcile stability, robustness, performance enhancement, reliability, actuator limitations, numerical andcomputational pitfalls, and tracking and regulation,faced to structured or unstructured system uncertainties. The potentials and eectiveness of the proposedmethod are substantiated by simulation results.
Quantitative design of robust control systems proposes a transparent and practical controller design methodology for uncertain single-input single-output and multivariable plants. There are several steps involved in the design of such controllers. The main steps involved in the design are template generation, loop shaping and pre-filter design. In the case of multivariable uncertain plants, manipulation of tolerance bounds within the available freedom, for both sequential and non-sequential designs, consideration of template size of next step in sequential design, and the appropriate selection of the nominal transfer function matrices in the equivalent disturbance attenuation design are also crucial steps. In all the quantitative designs, a time-consuming trial-and-error procedure is adapted and a successful compromise between various design requirements is very much dependent on the designer experience and expertise. In this paper, these steps are reformulated in terms of different cost functions, and it is shown that the optimization of these cost functions leads to an optimal design of quantitative controllers, for both single input single output and multivariable plants. This proposes a nonlinear constrained optimization problem that can be easily solved using any of the random optimization techniques. Simulation results are used to show the effectiveness of the proposed method.
A new sufficient condition is presented for the overall stability of decentralised linear control systems. This condition is in terms of the eigenvalues of the Hermitian part of the interaction matrix and the Hermitian part of the state matrix of each closed-loop isolated subsystem.
An algorithm to derive a multiple models set for a plant by the use of the self-organising map (SOM) were introduced by the authors (1999). The statistical properties of the models are investigated in this paper. As a plant, we consider a linear time invariant one. The parameters of the plant at each step are selected randomly with a specified distribution. Based on this distribution, the point distribution of the parameters of the multiple models is derived for this plant and compared with the plant parameters distribution.
A method for Multiple identification based on the self-organizing map neural networks is presented and some of its properties is investigated. Inputs to the NN are instantaneous parameters and so the reference vectors of the networks outputs are the parameter estimation of the multiple models.
Fuzzy logic controllers (FLC) has shown good performances on the controlling of the plants. In spite of this fact, FLC is an ill-defined function for analyzing. Fortunately GA is a good optimizer tools for the ill-defined functions, down to just measurable ones. From 1989 that Karr et al introduced the first GA optimizer for FLCs, it attracted many researchers. This paper overlooks these efforts in the last 10 years. It is not possible to write down the complete list of references have been used in this abstract. So we just mentioned the earliest and some of the other main references. A complete list and report are available from.
This paper deals with the design and implementation of a robust controller for the static VAR compensator (SVC) in remote industrial power system, to enhance the voltage profile for three phase single cage induction motor (SCIM) loads. The controller design is based on μ-synthesis method to deal with uncertainties arising in industrial network modeling. The performance of the controller has been evaluated extensively by non-linear time domain simulation. It is concluded that the robust controller enhances the voltage profile for SCIM loads compared with conventional SVC type (CSVC), which consist of voltage and current feedback loops.
A predictive fuzzy controller is designed and implemented for an industrial furnace. The furnace temperature is controlled so as to track the reference profiles accurately, and to reject the disturbances. A RLS on-line predictor is used to predict future values of the plant’s output. Using these predicted values, the future error values with respect to the reference profiles are evaluated. With regard to these errors, the fuzzy controller inferences the input power to be delivered to the furnace in order to eliminate the future tracking error.
Singular perturbation methods are used to demonstrate that the step-response matrices of linear multivariable systems containing small ‘parasitic” elements have a distinctive structure which guarantees the robustness of both non-adaptive and adaptive controllers for such systems incorporating step-response matrices. The significance of these results in relation to the modelling of multivariable plants with ‘fast” actuators and sensors is illustrated, and their validity is demonstrated by considering a typical gas-turbine jet engine.
Using the balanced realisations of a multivariable plant, input-output pairing can be achieved, which is the most suitable pairing for the design of decentralised, sequential closing type multivariable controllers. In the approach proposed by the authors, states are used as the interface variables between the inputs and the outputs of the plant.
The adaptive stabilizer can overcome the problem of parameter variations but will result in a very complex control system compared to the conventional or even state-feedback type stabilizers. It also has its own problems such as convergence or stability in the presence of unmodelled dynamics. To overcome the problem of parameter variations and to maintain the simplicity of the stabilizer for practical implementations, a robust power system stabiliser (PSS) is proposed in this paper using the quantitative feedback theory. In the present design the power system under consideration is a single synchronous generator connected to an infinite bus through a transmission line. The control ratio is modelled, the templates of the power system are formed for a wide range of frequencies, the U-Contour and different bounds for power system uncertainties are determined. After shaping the open-loop transfer function of the control system, the controller is finally designed.
Since many industrial processes are essentially linear multivariable type-one plants (i.e. linear multivariable plants with unbounded step-response matrices but with bounded impulse-response matrices), the methodologies of Porter and Jones (1986) for linear multivariable type-zero plants are extended to embrace such linear multivariable type-one plants. It is shown that the proportional and derivative controller matrices in the resulting PD controllers can be directly determined from open-loop impulse-response tests performed on linear multivariable type-one plants. The disturbance-rejection properties of these controllers are fully developed by modifying the digital PD controller by the inclusion of an outer PID loop. The robustness propertcs of these PD-PID controllers are assessed by characterizing, in terms of the steady-state impulse-response matrices of nominal and actual plants, the admissible plant perturbations that can be tolerated. The effectiveness of this design methodology is illustrated by designing a tunable digital set-point tracking PD-PID controller for a steel mill.
The robustness properties of tunable digital set-point tracking PID controllers are assessed. This assessment is effected by characterizing, in terms of the steady-state transfer function matrices of nominal and actual plants, the admissible plant perturbations that can be tolerated by such tunable digital PID controllers. The resulting robustness theorem is illustrated by designing an autopilot for a missile in the form of a tunable digital set-point tracking PID controller.
Marine vehicles, and hydrofoils in particular, are complex multivariable plants with significant levels of open-loop interaction. It is difficult to obtain explicit mathematical models for such vehicles, and the effort involved is frequently wasted because marine vehicles usually exhibit significant plant-parameter variations. There is therefore a requirement for a methodology for the design of digital controllers for marine vehicles which is simply applicable to highly interactive multivariable plants, does not require explicit mathematical models, and is equally applicable to both fixed-parameter and variable-parameter plants. In order to circumvent the need for mathematical models in either state space or transfer function matrix form, and to avoid performance degradation, Jones and Porter (1987) introduced adaptive digital PID controllers. Such adaptive controllers incorporate online recursive identifiers which provide updated step-response matrices for inclusion in control laws. The effectiveness of these controllers for marine vehicles is illustrated by designs of both tunable and adaptive digital set-point tracking PID controllers for a four-input/four-output dynamically complex hydrofoil.
In order to circumvent the need for mathematical models of multivariable plants expressed in either state-space or transfer function matrix form, tunable digital PID controllers were introduced by Porter and Jones (1986). The controller matrices can be directly determined from open-loop tests performed on asymptotically stable plants. However, although such controllers are intrinsically robust, some degradation in closed-loop behaviour inevitably occurs in the case of large plant parameter variations. In order to avoid such performance degradation, adaptive digital PID controllers were therefore introduced by Jones and Porter (1987). Such controllers incorporate fast online recursive identifiers which provide updated step-response matrices for inclusion in control laws with the structure of the earlier controllers. The effectiveness of these controllers is illustrated by examples of tunable and adaptive digital set-point tracking PID controllers for a three-input/three-output dynamically complex warship.
It is shown that, by incorporating on-line recursive identifiers to provide updated steady-state plant transfer function matrices for inclusion in digital proportional-plus-integral control laws, highly robust adaptive digital set-point tracking PI controllers can be readily designed for nonminiraum-phase multivariable plants. The effectiveness of this methodology in the absence of precise a priori information concerning plant order is illustrated by designing an adaptive digital set-point tracking PI controller for a distillation column with nonminimum-phase characteristics using both exactly parametrised and grossly underparametrised models.
It is shown that, by incorporating fast on-line recursive identifiers to provide updated step-response matrices for inclusion in digital proportional-plus-integral control laws, highly robust adaptive digital setpoint tracking PI controllers can be readily designed for multivariable plants. The effectiveness of this methodology is illustrated by designing an adaptive digital setpoint tracking PI controller for a gas turbine using both exactly parametrised and grossly under-parametrised models.
The aim of this study is to prove validity of feedback error learning rule for a linear representation of dynamic system with unknown parameters. A simple single-layer neural network is assumed as an