Abstract: | Title | Year |
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Abstract: 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. | Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints L Edalati, A Khaki Sedigh, M Aliyari Shooredeli, A Moarefianpour Mechanical Systems and Signal Processing | 2018 |
Abstract: 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. | Applying a modified version of Lyapunov exponent for cancer diagnosis in biomedical images: the case of breast mammograms H Khodadadi, A Khaki-Sedigh, M Ataei, MR Jahed-Motlagh Multidimensional Systems and Signal Processing | 2018 |
Abstract: 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. | Detection and Isolation of Interior Defects Based on Image Processing and Neural Networks: HDPE Pipeline Case Study Shiva Safari, Mahdi Aliyari Shoorehdeli Journal of Pipeline Systems Engineering and Practice | 2018 |
Abstract: 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. | Generalization of parity space to fault detection based on Takagi‐Sugeno fuzzy models for non‐linear dynamic systems Majid Ghaniee Zarch, Mahdi Aliyari Shoorehdeli Expert Systems | 2018 |
Abstract: 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. | Reservoir characterization in under-balanced drilling using low-order lumped model Amirhossein Nikoofard, Tor Arne Johansen, Glenn-Ole Kaasa Journal of Process Control | 2018 |
Abstract: 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. | Robust estimation of ARX models with time varying time delays using variational Bayesian approach Yujia Zhao, Alireza Fatehi, Biao Huang IEEE transactions on cybernetics | 2018 |
Abstract: 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. | A data-driven hybrid ARX and markov chain modeling approach to process identification with time-varying time delays Yujia Zhao, Alireza Fatehi, Biao Huang IEEE Transactions on Industrial Electronics | 2017 |
Abstract: 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. | Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification Pedram Agand, Mahdi Aliyari Shoorehdeli, Ali Khaki-Sedigh Engineering Applications of Artificial Intelligence | 2017 |
Abstract: 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. | Adaptive Tuning of Model Predictive Control Parameters based on Analytical Results Tahereh Gholaminejad, Ali Khaki-Sedigh, Peyman Bagheri AUT Journal of Modeling and Simulation | 2017 |
Abstract: 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. | Comments on “A Novel Fault Diagnostics and Prediction Scheme Using a Nonlinear Observer With Artificial Immune System as an Online Approximator” L Mahmoodi, M Aliyari Shoorehdeli IEEE Transactions on Control Systems Technology | 2017 |
Abstract: 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. | Decentralized model reference adaptive control for interconnected time delay systems with delay in state and compensation of long delay in input by nested prediction Seyed Hamid Hashemipour, Nastaran Vasegh, Ali Khaki Sedigh AUT Journal of Modeling and Simulation | 2017 |
Abstract: 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. | Decentralized MRAC for Large-Scale Interconnected Systems With State and Input Delays by Integrators Inclusion SH Hashemipour, N Vasegh, AK Sedigh Journal of Dynamic Systems, Measurement, and Control | 2017 |
Abstract: 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. | Direct adaptive model predictive control tuning based on the first-order plus dead time models T Gholaminejad, A Khaki-Sedigh, P Bagheri IET Control Theory & Applications | 2017 |
Abstract: 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. | Ethics: An indispensable dimension in the university rankings Ali Khaki Sedigh Science and engineering ethics | 2017 |
Abstract: 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. | Input-constrained multi-model unfalsified switching control MN Manzar, G Battistelli, AK Sedigh Automatica | 2017 |
Abstract: 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. | Kalman filtering approach to multi-rate information fusion in the presence of irregular sampling rate and variable measurement delay Alireza Fatehi, Biao Huang Journal of Process Control | 2017 |
Abstract: 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. | Multi-linear model set design based on the nonlinearity measure and H-gap metric Davood Shaghaghi, Alireza Fatehi, Ali Khaki-Sedigh ISA transactions | 2017 |
Abstract: 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. | New H 2 filtering for descriptor systems: Singular and normal filters Sahereh Beidaghi, Ali Akbar Jalali, Ali Khaki Sedigh International Journal of Control, Automation and Systems | 2017 |
Abstract: 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. | Nonlinear Analysis of the Contour Boundary Irregularity of Skin Lesion Using Lyapunov Exponent and KS Entropy Hamed Khodadadi, Ali Khaki Sedigh, Mohammad Ataei, Mohammad Reza Jahed Motlagh, Ali Hekmatnia Journal of Medical and Biological Engineering | 2017 |
Abstract: 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. | Optimal distributed interconnectivity of multi-robot systems by spatially-constrained clustering Mahdi Aliyari Sh Matin Macktoobian Adaptive Behavior | 2017 |
Abstract: 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. | Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual S Hamideh Sedigh Ziyabari, Mahdi Aliyari Shoorehdeli International Journal of Control, Automation and Systems | 2017 |
Abstract: 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. | Robust H∞ filtering for uncertain discrete-time descriptor systems Sahereh Beidaghi, Ali Akbar Jalali, Ali Khaki Sedigh, Bijan MoaveniInternational Journal of Control, Automation and Systems International Journal of Control, Automation and Systems | 2017 |
Abstract: 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. | Self‐falsification in multimodel unfalsified adaptive switching control M Nouri Manzar, A Khaki‐Sedigh nternational Journal of Adaptive Control and Signal Processing | 2017 |
Abstract: 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 and Parameter Estimation of a Drift-Flux Model for Underbalanced Drilling Operations Amirhossein Nikoofard, Ulf Jakob F Aarsnes, Tor Arne Johansen, Glenn-Ole Kaasa IEEE Transactions on Control Systems Technology | 2017 |
Abstract: 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. | State Estimation and Fusion in the Presence of Integrated Measurement Alireza Fatehi, Biao Huang IEEE Transactions on Instrumentation and Measurement | 2017 |
Abstract: 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. | Attention control learning in the decision space using state estimation Zahra Gharaee, Alireza Fatehi, Maryam S Mirian, Majid Nili Ahmadabadi International Journal of Systems Science | 2016 |
Abstract: 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. | Constrained Dynamic Control Allocation in the Presence of Singularity and Infeasible Solutions David Buzorgnia, Ali Khaki-Sedigh arXiv preprint arXiv:1607.05209 | 2016 |
Abstract: 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. | Control performance assessment based on sensor fusion techniques S Afshar Khamseh, A Khaki Sedigh, B Moshiri, A Fatehi Control Engineering Practice | 2016 |
Abstract: 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. | Fault tolerant control design using adaptive control allocation based on the pseudo inverse along the null space SS Tohidi, A Khaki Sedigh, D Buzorgnia International Journal of Robust and Nonlinear Control | 2016 |
Abstract: 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. | Fuzzy robust fault estimation scheme for a class of nonlinear systems based on an unknown input sliding mode observer Hamideh Sedigh Ziyabari, Mahdi Aliyari Shoorehdeli Journal of Vibration and Control | 2016 |
Abstract: 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. | Hybrid Concepts of the Control and Anti-Control of Flexible Joint Manipulator Mojtaba Rostami Kandroodid, Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Maysam Zamani Pedram International Journal of Robotics, Theory and Applications | 2016 |
Abstract: 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. | Immersion and invariance adaptive velocity observer for a class of Euler–Lagrange mechanical systems Mehdi Tavan, Ali Khaki-Sedigh, Mohammad-Reza Arvan, Ahmad-Reza Vali Nonlinear Dynamics | 2016 |
Abstract: 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. | Indirect adaptive hierarchical fuzzy sliding mode controller for a class of nonlinear systems MA Shoorehdeli M Mansouri, M Teshnehlab Journal of Intelligent & Fuzzy Systems | 2016 |
Abstract: 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. | Low-complexity control of hybrid systems using approximate multi-parametric MILP Jalal Habibi, Behzad Moshiri, Ali Khaki Sedigh, Manfred Morari Automatica | 2016 |
Abstract: 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. | On-line Full Probability Distribution Identification of ARX Model Parameters Based on Bayesian Approach Amir HoseinValadkhani, Aminollah Khormali, Mahdi Aliyari Shoorehdeli Hamid Khaloozadeh Alireza Fatehi IFAC-PapersOnLine | 2016 |
Abstract: 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. | Robust Fractional Order PI Controller Tuning Based on Bode’s Ideal Transfer Function Roohallah Azarmi, Mahsan Tavakoli-Kakhki, Ali Khaki Sedigh, Alireza Fatehi IFAC-PapersOnLine | 2016 |
Abstract: 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. | Robust Gaussian process modeling using EM algorithm Rishik Ranjan, Biao Huang, Alireza Fatehi Journal of Process Control | 2016 |
Abstract: 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. | Stability analysis of particle dynamics in gravitational search optimization algorithm Faezeh Farivar, Mahdi Aliyari Shoorehdeli Information Sciences | 2016 |
Abstract: 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. | Time-variant artificial potential field (TAPF): a breakthrough in power-optimized motion planning of autonomous space mobile robots Matin Macktoobian, Mahdi Aliyari Shoorehdeli Robotica | 2016 |
Abstract: 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. | A modified independent component analysis-based fault detection method in plant-wide systems Mazdak Teimoortashloo, Ali Khaki Sedigh Control and Cybernetics | 2015 |
Abstract: 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. | A novel alignment repulsion algorithm for flocking of multi-agent systems based on the number of neighbours per agent R Kahani, AK Sedigh, M Gh Mahjani International Journal of Control | 2015 |
Abstract: 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. | Adaptive Simplified Model Predictive Control with Tuning Considerations AS Ashtari, A Khaki Sedigh AUT Journal of Electrical Engineering | 2015 |
Abstract: 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. | Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems Mahdi Aliyari Shoorehdeli Mohammad Mansouri, Mohammad Teshnehlab ISA transactions | 2015 |
Abstract: 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. | An indirect adaptive predictive control for the pitch channel autopilot of a flight system Karim Salahshoor, Ali Khaki-Sedigh, Pouria Sarhadi Aerospace Science and Technology | 2015 |
Abstract: 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. | Analytical design of fractional order PID controllers based on the fractional set-point weighted structure: Case study in twin rotor helicopter Roohallah Azarmi, Mahsan Tavakoli-Kakhki, Ali Khaki Sedigh, Alireza Fatehi Mechatronics | 2015 |
Abstract: 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. | Closed form tuning equations for model predictive control of first-order plus fractional dead time models Peyman Bagheri, Ali Khaki-Sedigh International Journal of Control, Automation and Systems | 2015 |
Abstract: 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. | Control of Multichaotic Systems Using the Extended OGY Method Ensieh Nobakhti, Ali Khaki-Sedigh, Nastaran Vasegh International Journal of Bifurcation and Chaos | 2015 |
Abstract: 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. | Control performance assessment for a class of nonlinear systems using second-order Volterra series models based on nonlinear generalised minimum variance con... Mohsen Maboodi, Ali Khaki-Sedigh, Eduardo F Camacho International Journal of Control | 2015 |
Abstract: 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. | Decentralized MRAC for Large Scale Systems with Input and State Delays Syed Hamid Hashemipour, Nastrn Vasegh, Ali Khaki Sedigh The Modares Journal of Electrical Engineering | 2015 |
Abstract: 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. | Designing a new framework using type-2 FLS and cooperative-competitive genetic algorithms for road detection from IKONOS satellite imagery Maryam Nikfar, Mohammad Javad Valadan Zoej, Mehdi Mokhtarzade, Mahdi Aliyari Shoorehdeli Remote Sensing | 2015 |
Abstract: 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. | Estimation of states and parameters of a drift-flux model with unscented Kalman filter Amirhossein Nikoofard, Ulf Jakob F Aarsnes, Tor Arne Johansen, Glenn-Ole Kaasa IFAC-PapersOnLine | 2015 |
Abstract: 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. | Evaluation of Lyapunov-based adaptive observer using low-order lumped model for estimation of production index in under-balanced drilling Amirhossein Nikoofard, Tor Arne Johansen, Glenn-Ole Kaasa IFAC-PapersOnLine | 2015 |
Abstract: 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. | Implementation of an Improved Performance Integral H_2/H_∞ Combined Predictive Control on a GSP Mahdy Rezaei Darestani, AmirAli Nikkhah, Ali KhakiSedigh The Modares Journal of Electrical Engineering | 2015 |
Abstract: 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. | Latency Compensation in Multi Chaotic Systems Using the Extended OGY Control Method Ensieh Nobakhti, Ali Khaki Sedigh AUT Journal of Modeling and Simulation | 2015 |
Abstract: 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. | MULTI-MODEL UNFALSIFIED PREDICTIVE SUPERVISORY CONTROL MANZAR MOJTABA NOURI, SEDIGH ALI KHAKI JOURNAL OF CONTROL | 2015 |
Abstract: 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. | Non-linear generalised minimum variance control state space design for a second-order Volterra series model Mohsen Maboodi, Eduardo F Camacho, Ali Khaki-Sedigh International Journal of Systems Science | 2015 |
Abstract: 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. | Non-monotonic robust H2 fuzzy observer-based control for discrete time nonlinear systems with parametric uncertainties Siavash Fakhimi Derakhshan, Alireza Fatehi International Journal of Systems Science | 2015 |
Abstract: 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. | Operating condition diagnosis based on HMM with adaptive transition probabilities in presence of missing observations Nima Sammaknejad, Biao Huang, Weili Xiong, Alireza Fatehi, Fangwei Xu, Aris Espejo AIChE Journal | 2015 |
Abstract: 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. | Robust tuning of dynamic matrix controllers for first order plus dead time models Peyman Bagheri, Ali Khaki Sedigh Applied Mathematical Modelling | 2015 |
Abstract: 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. | Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques Hamed Dehghan Banadaki, Hasan Abbasi Nozari, Mahdi Aliyari Shoorehdeli Thermal Science | 2015 |
Abstract: 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. | X–Y pedestal: partial quasi-linearization and cascade-based global output feedback tracking control Mehdi Tavan, Ali Khaki-Sedigh, Mohammad Reza Arvan, Ahmad Reza Vali Nonlinear Dynamics | 2015 |
Abstract: 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. | A comparison of alternative strategies for optimal utilisation of tyre friction forces aimed at vehicle lateral-plane motion control Javad Ahmadi, Ali Khaki-Sedigh International Journal of Vehicle Design | 2014 |
Abstract: 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. | A neutral system approach to H∞ PD/PI controller design of processes with uncertain input delay A Shariati, HD Taghirad, A Fatehi Journal of Process Control | 2014 |
Abstract: 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. | Adaptive monitoring of the process operation based on symbolic episode representation and hidden Markov models with application toward an oil sand primary separation Nima Sammaknejad, Biao Huang, Alireza Fatehi, Yu Miao, Fangwei Xu, Aris Espejo Computers & Chemical Engineering | 2014 |
Abstract: 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. | An Algorithm for Multi-Realization of Nonlinear MIMO Systems Soodeh Faraji, Ali Khaki Seddigh International Journal of Smart Electrical Engineering | 2014 |
Abstract: 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. | An analytical tuning approach to multivariable model predictive controllers Peyman Bagheri, Ali Khaki-Sedigh Journal of Process Control | 2014 |
Abstract: 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. | Analysis of a chaotic memristor based oscillator F Setoudeh, A Khaki Sedigh, M Dousti Abstract and Applied Analysis | 2014 |
Abstract: 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. | Application of augmented UD identification with selective forgetting in an adaptive control loop Pouria Sarhadi, Karim Salahshoor, Ali Khaki-Sedigh International Journal of Modelling, Identification and Control | 2014 |
Abstract: 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. | Control of cardiac arrhythmia by nonlinear spatiotemporal delayed feedback Forough Rezaei Boroujeni, Nastaran Vasegh, Ali Khaki Sedigh International Journal of Bifurcation and Chaos | 2014 |
Abstract: 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. | Control of Flexible Joint Manipulator via Variable Structure Rule-Based Fuzzy Control and Chaos Anti-Control with Experimental Validation KANDROODI MOJTABA ROSTAMI, Faezeh Farivar, SHOOREHDELI MAHDI ALIYARI INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING | 2014 |
Abstract: 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. | Design of set-point weighting PIλ+ Dμ controller for vertical magnetic flux controller in Damavand tokamak H Rasouli, A Fatehi Review of Scientific Instruments | 2014 |
Abstract: 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. | Evolutionary Algorithms to Compute the Optimal Parameters of Gaussian Radial Basis Adaptive Backstepping Control for Chaotic Systems Faezeh Farivar, Mohammad Ali Nekoui, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab Universal Journal of Control and Automation | 2014 |
Abstract: 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. | H∞/Predictive output control of a three-axis gyrostabilized platform Mahdy Rezaei Darestani, Amir Ali Nikkhah, Ali Khaki Sedigh Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering | 2014 |
Abstract: 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). | Multidisciplinary modelling and position tracking control of an electro-hydrostatic actuator using a novel adaptive fuzzy-PID controller Mohammad Javad Mirshojaeian Hosseini, Mahdi Aliyari Shoorehdeli International Journal of Advanced Mechatronic Systems | 2014 |
Abstract: 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. | Neural Networks for Normative Knowledge Source of Cultural Algorithm Vahid Seydi Ghomsheh, Mohamad Teshnehlab, Mahdi Aliyari Shoorehdeli, Mojaba Ahmadieh Khanesar International Journal of Computational Intelligence Systems | 2014 |
Abstract: 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. | Non-monotonic Lyapunov functions for stability analysis and stabilization of discrete time Takagi-Sugeno fuzzy systems Siavash Fakhimi Derakhshan, Alireza Fatehi Int. J. Innov. Comput. Inf. Control | 2014 |
Abstract: 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. | Nonmonotonic observer-based fuzzy controller designs for discrete time TS fuzzy systems via LMI Siavash Fakhimi Derakhshan, Alireza Fatehi, Mehrad Ghasem Sharabiany IEEE transactions on cybernetics | 2014 |
Abstract: 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. | Real-time dynamic HIL simulator of gas turbine, governor, generator and grid for static excitation of a 200-MVA synchronous generator Mohamad Esmaeil Iranian, Iman Yousefi, Mahdi Aliyari Shoorehdeli Simulation | 2014 |
Abstract: 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. | Review of Model Predictive Control Tuning Methods and Modern Tuning Solutions Ali Khaki Sedigh, Peyman Bagheri Journal of Control | 2014 |
Abstract: 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. | Robust second order sliding mode control for a quadrotor considering motor dynamics Nader Jamali Soufi Amlashi, Mohammad Rezaei, Hossein Bolandi, Ali Khaki Sedigh International Journal of Control Theory and Computer Modeling | 2014 |
Abstract: 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. | Design and Comparison of Constrained MPC With PID Controller for Heave Disturbance Attenuation in Offshore Managed Pressure Drilling Systems Amirhossein Nikoofard, Tor Arne Johansen, Hessam Mahdianfar, Alexey Pavlov Marine Technology Society Journal | 2014 |
Abstract: 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. | Switching-Tuning Adaptive Multiple Model Predictive Control Ali Shamsaddinlou, Alireza Fatehi, Ali Khaki Sedigh Journal of Control Engineering and Technology | 2014 |
Abstract: 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. | Analytical approach to tuning of model predictive control for first-order plus dead time models Peyman Bagheri, Peyman Bagheri Ali, Ali Khaki Sedigh, Khaki Sedigh IET Control Theory & Applications | 2013 |
Abstract: 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. | Automatic model bank selection in multiple model identification of gas turbine dynamics SeyedM Hosseini, Alireza Fatehi, Ali K Sedigh, Tor A Johansen Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering | 2013 |
Abstract: 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. | HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens Hamed Ahmadi, Ali Ahmadi, Sadegh Azimzadeh-Jamalkandi, Mahdi Aliyari Shoorehdeli, Ali Salehzadeh-Yazdi, Gholamreza Bidkhori, Ali Masoudi-Nejad Genomics | 2013 |
Abstract: 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. | Identification of non-linear predictor and simulator models of a cement rotary kiln by locally linear neuro-fuzzy technique Masoud Sadeghian, Alireza Fatehi International Journal of Mechatronics and Automation 2 | 2013 |
Abstract: 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. | Implementation and Control of X–Y Pedestal Using Dual-Drive Technique and Feedback Error Learning for LEO Satellite Tracking Taheri, A.,M.Aliyari Sh., H. Bahrami, M.H. Fatehi Control Systems Technology, IEEE Transactions on | 2013 |
Abstract: 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. | Mobile robot control based on neural network and feedback error learning approach H Zarabadipour, Z Yaghoubi, M Aliyari Shoorehdeli strategies | 2013 |
Abstract: 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. | Modified projective synchronization of unknown chaotic dissipative gyroscope systems via Gaussian radial basis adaptive variable structure control Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Mohammad Ali Nekoui Journal of Vibration and Control | 2013 |
Abstract: 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. | Nonlinear System Identification in Frequent and Infrequent Operating Points for Nonlinear Model Predictive Control Alireza Fatehi, Behnaneh Sadeghpour, Batool Labibi Information Technology and Control | 2013 |
Abstract: 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. | Patterns with different phases but same statistics Peyman Sheikholharam Mashhadi, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab JOSA A | 2013 |
Abstract: 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. | Position Control of a Pulse Width Modulated Pneumatic Systems: an Experimental Comparison Mahdi Aliyari Shoorehdeli, Farid Najafi, Sahar Jafari International Journal of Robotics, Theory and Applications | 2013 |
Abstract: 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. | Predictive Controlled GSP Performance Improvement with an Integrated ℋ2/ℋ∞ MR Darestani, AA Nikkhah, AK Sedigh IJE TRANSACTIONS B: Applications | 2013 |
Abstract: 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. | Robust Multivariable controller Design with the simultaneous H2/H [infinity]/µ for Single Person Aircraft Javad Mashayekhi Fard, Mohammad Ali Nekoui, Ali Khaki Sedigh, Roya Amjadifard International Journal of Electrical and Computer Engineering | 2013 |
Abstract: 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. | Selection of Sensors for Hydro-Active Suspension System of Passenger Car With Input–Output Pairing Considerations Ehsan Sarshari, Ali Khaki Sedigh Journal of Dynamic Systems, Measurement, and Control | 2013 |
Abstract: 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. | Short-term traffic flow forecasting: parametric and nonparametric approaches via emotional temporal difference learning Javad Abdi, Behzad Moshiri, Baher Abdulhai, Ali Khaki Sedigh Neural Computing and Applications | 2013 |
Abstract: 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. | Specificity enhancement in classification of breast MRI lesion based on multi-classifier Farzaneh Keyvanfard, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ke Nie, Min-Ying Su Neural Computing and Applications | 2013 |
Abstract: 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. | Synchronization of underactuated unknown heavy symmetric chaotic gyroscopes via optimal Gaussian radial basis adaptive variable structure control Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Ali Nekoui, Mohammad Teshnehlab IEEE Transactions on control systems technology | 2013 |
Abstract: 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. | The Scrutiny of Variation in the Number of Fuzzy Rules and Membership Functions in a New Genetic-Fuzzy System in Approximation and Prediction Problems Vahideh Keikha, Hayat Khoobipour, Mahdi Aliyari Shoorehdeli, Hassan Rezaei International Journal of Information and Electronics Engineering | 2013 |
Abstract: 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. | Two new methods for DNA splice site prediction based on neuro-fuzzy network and clustering Fahimeh Moghimi, Mohammad Taghi Manzuri Shalmani, Ali Khaki Sedigh, Mohammad Kia Neural Computing and Applications | 2013 |
Abstract: 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. | Type-2 fuzzy control for a flexible-joint robot using voltage control strategy Majid Moradi Zirkohi, Mohammad Mehdi Fateh, Mahdi Aliyari Shoorehdeli International Journal of Automation and Computing | 2013 |
Abstract: 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. | Adaptive neural network control of bilateral teleoperation with constant time delay A Forouzantabar, HA Talebi, AK Sedigh Nonlinear Dynamics | 2012 |
Abstract: 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. | An Indirect Adaptive Predictive Control with Augmented UD Identifier for Linear Time Varying Systems Pouria Sarhadi, Karim Salahshoor, Ali Khaki-Sedigh International Journal of Computer and Electrical Engineering | 2012 |
Abstract: 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. | An interdisciplinary overview and intelligent control of human prosthetic eye movements system for the emotional support by a huggable pet-type robot from a biomechatronical viewpoint Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab Journal of the Franklin Institute | 2012 |
Abstract: 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. | An under load servo actuator identification and comparison between the results of different methods M Maboodi, MH Ashtari Larki, M Aliyari Shoorehdeli Iranian Journal of Electrical & Electronic Engineering | 2012 |
Abstract: 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) [14] 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. | Bilateral control of master–slave manipulators with constant time delay A Forouzantabar, HA Talebi, AK Sedigh ISA transactions | 2012 |
Abstract: 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. | Breast cancer classification based on advanced multi dimensional fuzzy neural network Somayeh Naghibi, Mohammad Teshnehlab, Mahdi Aliyari Shoorehdeli Journal of medical systems | 2012 |
Abstract: 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. | Chaos control and generalized projective synchronization of heavy symmetric chaotic gyroscope systems via Gaussian radial basis adaptive variable structure control Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Ali Nekoui, Mohammad Teshnehlab Chaos, Solitons & Fractals | 2012 |
Abstract: 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. | Chaos control and modified projective synchronization of unknown heavy symmetric chaotic gyroscope systems via Gaussian radial basis adaptive backstepping control Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Ali Nekoui, Mohammad Teshnehlab Nonlinear Dynamics | 2012 |
Abstract: 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. | Combined Enhanced LMI Charactrization and Parametric Eigenstructure Assignment Using Static Output Feedback Amir Parviz Valadbeygi, Ali Khaki Sedigh, Saeed Hosseinnia Journal of Intelligent Procedures in Electrical Technology | 2012 |
Abstract: 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. | Control of flexible joint manipulator via reduced rule-based fuzzy control with experimental validation Mojtaba Rostami Kandroodi, Mohammad Mansouri, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab ISRN Artificial Intelligence | 2012 |
Abstract: 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. | Decentralized supervisory based switching control for uncertain multivariable plants with variable input–output pairing Omid Namaki-Shoushtari, Ali Khaki-Sedigh ISA transactions | 2012 |
Abstract: 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. | Design of a prediction model for cement rotary kiln using wavelet projection fuzzy inference system A Sharifi, M Aliyari Shoorehdeli, Mohammad Teshnehlab Cybernetics and Systems | 2012 |
Abstract: 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. | Design of an Optimal Fuzzy Controller for Hover mode of Finite Degree of Freedom Unmanned Helicopter Sina Ameli, Alireza Fatehi, Mohamad Ataei Journal of Intelligent Procedures in Electrical Technology | 2012 |
Abstract: 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. | Energy reduction with anticontrol of chaos for nonholonomic mobile robot system Zahra Yaghoubi, Hassan Zarabadipour, Mahdi Aliyari Shoorehdeli Abstract and Applied Analysis | 2012 |
Abstract: 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. | Enhancement of Robust Tracking Performance via Switching Supervisory Adaptive Control SHOUSHTARI O NAMAKI, SEDIGH A KHAKI IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING | 2012 |
Abstract: 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. | Fault Detection in Tennessee Eastman Process Using Fisher’s Discriminant Analysis and Principal Component Analysis Modified by Genetic Algorithm Mostafa Noruzi Nashalji, Seyed Mohammad Razeghi, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab Applied Mechanics and Materials | 2012 |
Abstract: 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. | Fault Detection of Nonlinear Systems Based on Takagi-Sugeno Fuzzy Models by Parity Relations Majid Ghaniee Zarch, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab A Review of the State of the Art of Modulation Techniques and Control Strategies for Matrix Converters | 2012 |
Abstract: 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. | Fault tolerant control of mechatronics system based on hybrid control Atefeh Saedian, Hassan Zarabadipour, Mahdi Aliyari Shoorehdeli, Faezeh Farivar International Journal of Physical Sciences | 2012 |
Abstract: 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. | Fault tolerant synchronization of chaotic heavy symmetric gyroscope systems versus external disturbances via Lyapunov rule-based fuzzy control Faezeh Farivar, Mahdi Aliyari Shoorehdeli ISA transactions | 2012 |
Abstract: 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. | Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm Javad Abdi, Behzad Moshiri, Baher Abdulhai, Ali Khaki Sedigh Engineering Applications of Artificial Intelligence | 2012 |
Abstract: 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. | Identification of cement rotary kiln using hierarchical wavelet fuzzy inference system A Sharifi, M Aliyari Shoorehdeli, M Teshnehlab Journal of the Franklin Institute | 2012 |
Abstract: 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. | IMPLEMENTATION OF AN IMPROVED PERFORMANCE INTEGRAL H2/H∞ COMBINED PREDICTIVE CONTROL ON A GSP DARESTANI MAHDY REZAEI, AMIR ALI NIKKHAH, SEDIGH ALI KHAKI MODARES JOURNAL OF ELECTRICAL ENGINEERING | 2012 |
Abstract: 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. | Improved switching for multiple model adaptive controller in noisy environment Pouya Bashivan, Alireza Fatehi Journal of Process Control | 2012 |
Abstract: 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. | Intelligent prediction of differential pipe sticking by support vector machine compared with conventional artificial neural networks: An example of iranian offshore oil fields Reza Jahanbakhshi, Reza Keshavarzi, Mahdi Aliyari Shoorehdeli, Abolqasem Emamzadeh SPE Drilling & Completion | 2012 |
Abstract: 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. | Inverted Pendulum Fault Tolerant Control Based on Fuzzy Backstepping Design and Anti-Control of Chaos Atefeh Saedian, Hassan Zarabadipour, Mahdi Aliyari Shoorehdeli, Faezeh Farivar IFAC Proceedings Volumes | 2012 |
Abstract: 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. | Lyapunov based control of flexible joint manipulator with experimental validation by using chaotic gyroscope synchronization Mojtaba Rostami Kandroodi, Faezeh Farivar, Mahdi Aliyari Shoorehdeli International Journal of Mechanic Systems Engineering | 2012 |
Abstract: 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. | Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques Hasan Abbasi Nozari, Mahdi Aliyari Shoorehdeli, Silvio Simani, Hamed Dehghan Banadaki Neurocomputing | 2012 |
Abstract: 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. | Modelling and Control of Four-Wheel Anti-lock Braking System MA Nekoui, A Khaki Sedigh Majlesi Journal of Electrical Engineering | 2012 |
Abstract: 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. | Modelling and Control of Four-Wheel Anti-lock Braking System. Javad Mashayekhi Fard, Mohammad Ali Nekoui, Ali Khaki Sedigh Majlesi Journal of Electrical Engineering | 2012 |
Abstract: 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. | Modified projective synchronization of chaotic dissipative gyroscope systems via backstepping control Faezeh Farivar, Mohammad Ali Nekoui, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab Indian Journal of Physics | 2012 |
Abstract: 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. | Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets Amir Hossein Nikoofard, Hossein Hajimirsadeghi, Ashkan Rahimi-Kian, Caro Lucas Applied Soft Computing | 2012 |
Abstract: 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). | Multiple model bank selection based on nonlinearity measure and H-gap metric SeyedMehrdad Hosseini, Alireza Fatehi, Tor Arne Johansen, Ali Khaki Sedigh Journal of Process Control | 2012 |
Abstract: … | On the structural optimization of a neural network model predictive controller Mahsa Sadeghassadi, Alireza Fatehi, Ali Khaki Sedigh, SeyedMehrdad Hosseini Industrial & Engineering Chemistry Research | 2012 |
Abstract: 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. | Robust tracking of a class of perturbed nonlinear systems via multivariable nested sliding mode control Aras Adhami-Mirhosseini, Mohammad J Yazdanpanah, Ali Khaki-Sedigh Journal of Dynamic Systems, Measurement, and Control | 2012 |
Abstract: 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. | Robust Water Level Control of the U-Tube Steam Generator O Safarzadeh, A Khaki-Sedigh, AS Shirani Journal of Energy Engineering | 2012 |
Abstract: 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. | Robustification of input redundant feedback systems using robust actuator weighting in the control allocation problem Javad Ahmadi, Ali Khaki-Sedigh, Abdolreza Ohadi International Journal of Control | 2012 |
Abstract: 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. | Robustness analysis and tuning of generalized predictive control using frequency domain approaches P Sarhadi, K Salahshoor, A Khaki-Sedigh Applied Mathematical Modelling | 2012 |
Abstract: 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. | Stabilization of chaos systems described by nonlinear fractional-order polytopic differential inclusion Saeed Balochian, Ali Khaki Sedigh Chaos: An Interdisciplinary Journal of Nonlinear Science | 2012 |
Abstract: 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. | Sufficient condition for stabilization of linear time invariant fractional order switched systems and variable structure control stabilizers Saeed Balochian, Ali Khaki Sedigh ISA transactions | 2012 |
Abstract: 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. | Target tracking of autonomous robotic vehicles using range‐only measurements: a switched logic‐based control strategy Omid Namaki‐Shoushtari, A Pedro Aguiar, Ali Khaki‐Sedigh International Journal of Robust and Nonlinear Control | 2012 |
Abstract: 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. | Two-degree-of-freedom helicopter closed-loop identification through a cascade controller Pouya Ghalei, Alireza Fatehi, Mohamadreza Arvan Advanced Materials Research | 2012 |
Abstract: 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. | A predictive multi-agent approach to model systems with linear rational expectations Moeen Mostafavi, Ali-Reza Fatehi, G Shakouri, Peter Von zur Muehlen Munich Personal RePEc Archive | 2011 |
Abstract: 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. | A switched based control strategy for target tracking of autonomous robotic vehicles using range-only measurements Omid Namaki-Shoushtari, A Pedro Aguiar, Ali Khaki Sedigh IFAC Proceedings Volumes | 2011 |
Abstract: 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. | An under Load Servo Actuator Identification Mohsen Maboodi, MH Ashtari Larki, M Aliyari Shoorehdeli, Hosein Bolandi IFAC Proceedings Volumes | 2011 |
Abstract: 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. | Applying High Power Lasers in Perforating Oil and Gas Wells: Prediction of the Laser Power Loss During Laser Beam-Fluid Interaction by Using Artificial Neural Network R Keshavarzi, R Jahanbakhshi, H Bayesteh, A Ghorbani, MA Shoorehdeli Lasers in Engineering (Old City Publishing) | 2011 |
Abstract: 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. | Automatic pairing of large scale MIMO plants using normalised RGA Alireza Fatehi International Journal of Modelling, Identification and Control | 2011 |
Abstract: 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. | Comparison of nonlinearity measures based on time series analysis for nonlinearity detection Seyed Mehrdad Hosseini, Tor Arne Johansen, Alireza Fatehi Modeling, Identification and Control | 2011 |
Abstract: 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. | DELAYED FEEDBACK CONTROL OF DELAYED CHAOTIC SYSTEMS: NUMERICAL ANALYSIS OF BIFURCATION Ali Khaki Sedigh 6th EUROMECH Nonlinear Dynamics Conference (ENOC 2008) | 2011 |
Abstract: 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. | Descriptive vector, relative error matrix, and interaction analysis of multivariable plants Nima Monshizadeh-Naini, Alireza Fatehi, Ali Kahki-Sedigh Automatica | 2011 |
Abstract: 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. | Design of Supervisory Based Switching QFT Controllers with Bumpless Transfer Omid Namaki-Shoushtari, Ali Khaki Sedigh Journal of Control | 2011 |
Abstract: 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. | Design of switching control systems using control performance assessment index Arefeh Moridi, Shabnam Armaghan, Ali Khaki Sedigh, Saleheh Choobkar Proceedings of the World Congress on Engineering 2011 Vol II | 2011 |
Abstract: 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. | Designing flexible neuro-fuzzy system based on sliding mode Controller for magnetic levitation systems Zahra Mohammadi, Mohammad Teshnehlab, Mahdi Aliyari Shoorehdeli IJCSI International Journal of Computer Science Issues | 2011 |
Abstract: 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[1]. 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. | Feature selection and classification of breast cancer on dynamic magnetic resonance imaging using ANN and SVM F Keyvanfard, MA Shoorehdeli, M Teshnehlab American Journal of Biomedical Engineering | 2011 |
Abstract: 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. | Generalized projective synchronization of uncertain chaotic systems with external disturbance Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Ali Nekoui, Mohammad Teshnehlab Expert Systems with Applications | 2011 |
Abstract: 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. | Identification and robust water level control of horizontal steam generators using quantitative feedback theory O Safarzadeh, A Khaki-Sedigh, AS Shirani Energy Conversion and Management | 2011 |
Abstract: 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. | Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique Masoud Sadeghian, Alireza Fatehi Journal of Process Control | 2011 |
Abstract: 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. | Improved dead zone modification for robust adaptive control of uncertain linear systems described by input-output models with actuator faults Behnam Allahverdi Charandabi, Farzad R Salmasi, Ali Khaki Sedigh IEEE Transactions on Automatic Control | 2011 |
Abstract: 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. | Multiple model predictive control of multivariable ph process using adaptive weighting matrices Peyman Bagheri, Vahid Mardanlou, Alireza Fatehi IFAC Proceedings Volumes | 2011 |
Abstract: 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. | Shape optimization of electrostatically actuated micro cantilever beam with extended travel range using simulated annealing RR Trivedi, MM Joglekar, RP Shimpi, DN Pawaskar Proc. World Congr. Eng | 2011 |
Abstract: 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. | Stability of feedback error learning for linear systems Mehdi Tavan, Mahdi Aliyari Shoorehdeli, Amir Reza Zare Bidaki IFAC Proceedings Volumes | 2011 |
Abstract: 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 | Stabilization of fractional order systems using a finite number of state feedback laws Saeed Balochian, Ali Khaki Sedigh, Mohammad Haeri Nonlinear Dynamics | 2011 |
Abstract: 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 | Stabilization of multi-input hybrid fractional-order systems with state delay Saeed Balochian, Ali Khaki Sedigh, Asef Zare ISA transactions | 2011 |
Abstract: 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. | Tuning of dynamic matrix controller for FOPDT models using analysis of variance Peyman Bagheri, Ali Khaki-Sedigh IFAC Proceedings Volumes | 2011 |
Abstract: 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. | Variable structure control of linear time invariant fractional order systems using a finite number of state feedback law Saeed Balochian, Ali Khaki Sedigh, Asef Zare Communications in Nonlinear Science and Numerical Simulation | 2011 |
Abstract: 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. | Verification of intelligent control of a launch vehicle with HILS M Rezaei Darestani, M Zareh, J Roshanian, A Khaki Sedigh Journal of Mechanical Science and Technology | 2011 |
Abstract: 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. | A modified proportional navigation guidance for accurate target hitting A Moharampour, J Poshtan, A Khaki Sedigh Iranian Journal of Electrical and Electronic Engineering | 2010 |
Abstract: 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. | Adaptive control of chaos in cardiac arrhythmia SAMAREH ATTARSHARGHI, MOHAMMAD REZA JAHED-MOTLAGH, NASTARAN VASEGH, ALI KHAKI-SEDIGH Mechanical and Electronics Engineering | 2010 |
Abstract: 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. | An adjustable model reference adaptive control for a flexible launch vehicle AM Khoshnood, J Roshanian, AA Jafari, A Khaki-Sedigh journal of dynamic systems, measurement, and control | 2010 |
Abstract: 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. | An ANOVA based analytical dynamic matrix controller tuning procedure for FOPDT models Peyman Bagheri, Ali Khaki-Sedigh AUT Journal of Modeling and Simulation | 2010 |
Abstract: 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. | Classification of Multi-Class Datasets Using 2D Membership Functions in TSK Fuzzy System. Loghman Kaki, Mohammad Teshnehlab, Mahdi Aliyari Shoorehdeli Int. J. Adv. Comp. Techn. | 2010 |
Abstract: 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. | COMPARATIVE STUDIES OF THE LMS AND MSLMS ALGORITHMS CONVERGENCE SPEED IN THE ACTIVE NOISE CONTROL SYSTEMS ASHRAF ANVARI, MOHAMMAD HASAN SHENASA, SEDIGH ALI KHAKI JOURNAL OF ELECTRICAL ENGINEERING SCIENCE | 2010 |
Abstract: 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. | Fault detection of the Tennessee Eastman process using improved PCA and neural classifier Mostafa Noruzi Nashalji, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab Soft computing in industrial applications | 2010 |
Abstract: 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. | Flight control of a launch vehicle using an M Zareh, M Rezaei, J Roshanian, A Khaki-Sedigh Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering | 2010 |
Abstract: 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. | Generalized predictive control and tuning of industrial processes with second order plus dead time models AR Neshasteriz, A Khaki Sedigh, H Sadjadian Journal of Process Control | 2010 |
Abstract: 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. | Investigation of the Performance of MultipleModeling and Control Approach Using aLaboratory Helicopter V Nazarzehi, A Fatehi, M Zamanian International Journal of Computer and Electrical Engineering | 2010 |
Abstract: 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. | Neural Sliding Mode Control for Chaos Synchronization of Uncertain Nonlinear Gyros Faezeh Farivar, Mohammad Ali Nekoui, Mahdi Aliyari Shoorehdeli Advances and Applications in Mathematical Sciences | 2010 |
Abstract: 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. | Training health professionals to conduct family education for families of patients with first-episode psychosis: adherence to protocol Yasaman Mottaghipour, Niloofar Salesian, Arshia Seddigh, Mohsen Jalali Roudsari, Sahar Tahbaz Hosseinzade, Vandad Sharifi Iranian journal of psychiatry | 2010 |
Abstract: 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. | Why the determinacy condition is a weak criterion in rational expectations models Moeen Mostafavi, G Shakouri, Ali-Reza Fatehi Munich Personal RePEc Archive | 2010 |
Abstract: 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. | Abnormal condition detection in a cement rotary kiln with system identification methods Iman Makaremi, Alireza Fatehi, Babak Nadjar Araabi, Morteza Azizi, Ahmad Cheloeian Journal of process control | 2009 |
Abstract: 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. | Adaptive control of nonlinear in parameters chaotic system via Lyapunov exponents placement Moosa Ayati, Ali Khaki-Sedigh Chaos, Solitons & Fractals | 2009 |
Abstract: 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. | Adaptive vehicle lateral-plane motion control using optimal tire friction forces with saturation limits consideration Javad Ahmadi, Ali Khaki Sedigh, Mansour Kabganian IEEE Transactions on vehicular technology | 2009 |
Abstract: 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. | An evolutionary artifact rejection method for brain computer interface using ICA A Asadi Ghanbari, MR Nazari Kousarrizi, M Teshnehlab, M Aliyari International Journal of Electrical & Computer Sciences | 2009 |
Abstract: 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. | Chaos control in delayed chaotic systems via sliding mode based delayed feedback Nastaran Vasegh, Ali Khaki Sedigh Chaos, Solitons & Fractals | 2009 |
Abstract: 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. | Chaos control via TDFC in time-delayed systems: The harmonic balance approach Nastaran Vasegh, Ali Khaki Sedigh Physics Letters A | 2009 |
Abstract: 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). | Design of multiple model controller using SOM neural network P Bashivan, AR FATEHI JOURNAL OF CONTROL | 2009 |
Abstract: 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. | Gaussian radial basis adaptive backstepping control for a class of nonlinear systems Faezeh Farivar, M Aliyari Shoorehdeli, Mohammad Ali Nekoui, Mohammad Teshnehlab Applied Sci | 2009 |
Abstract: 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. | Generalized projective synchronization for chaotic systems via Gaussian radial basis adaptive backstepping control Faezeh Farivar, Mahdi Aliyari Shoorehdeli, Mohammad Ali Nekoui, Mohammad Teshnehlab Chaos, Solitons & Fractals | 2009 |
Abstract: 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. | Hybrid control of flexible manipulator F Farivar, M Aliyari Shoorehdeli, M Teshnehlab, MA Nekoui Journal of Applied Sciences | 2009 |
Abstract: 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. | Identification using ANFIS with intelligent hybrid stable learning algorithm approaches Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki Sedigh Neural Computing and Applications | 2009 |
Abstract: 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. | Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki Sedigh, M Ahmadieh Khanesar Applied Soft Computing | 2009 |
Abstract: 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. | Input− output pairing using effective relative energy array N Monshizadeh-Naini, A Fatehi, A Khaki-Sedigh Industrial & Engineering Chemistry Research | 2009 |
Abstract: 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. | Mineralogical and Geochemical Characteristics of Clay Deposits from South Abarkouh District of Clay Deposit (Central Iran) and Their Applications A.S. Mahjoor, M. Karimi and A. Rastegarlari Journal of Applied Sciences | 2009 |
Abstract: 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. | Observer-based adaptive control of chaos in nonlinear discrete-time systems using time-delayed state feedback Amin Yazdanpanah Goharrizi, Ali Khaki-Sedigh, Nariman Sepehri Chaos, Solitons & Fractals | 2009 |
Abstract: 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. | One year follow-up of patients with bipolar disorder admitted to Roozbeh Hospital Homayoun Amini, Vandad Sharifi, Aliakbar Nejatisafa, Mohammad Arbabi, Maryam Tabatabaie, Zeinab Alimadadi, Farhad Faridhosseini, Mahdi Rafiei Milajerdi, Alireza Manouchehri, Arshia Seddige Iran J Psychiatr Clin Psychol | 2009 |
Abstract: 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). | Optimal control of a nonlinear fed-batch fermentation process using model predictive approach Ahmad Ashoori, Behzad Moshiri, Ali Khaki-Sedigh, Mohammad Reza Bakhtiari Journal of Process Control | 2009 |
Abstract: 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. | PH control of penicillin fermentation process using predictive approach A Ashoori, B Moshiri, A Ramezani, M Reza Bakhtiari, A Khaki-Sedigh Systems Science | 2009 |
Abstract: 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. | POWER SYSTEM STABILITY IMPROVEMENT USING QFT-BASED EXCITATION ROBUST CONTROL FOROUD A AKBARI, HOSSEIN SEYFI, SEDIGH A KHAKI NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN) | 2009 |
Abstract: 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. | RANGE ESTIMATION FOR IR HOMING MISSILES A Moharampour, J Poshtan, SEDIGH A KHAKI AEROSPACE MECHANICS JOURNAL | 2009 |
Abstract: 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. | Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system Kamel Sabahi, Mohammad Teshnehlab Energy Conversion and Management | 2009 |
Abstract: 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. | Self-referential reasoning in the light of extended truth qualification principle Mohammad Reza Rajati, Hamid Khaloozadeh, Alireza Fatehi Intelligent Engineering Systems and Computational Cybernetics | 2009 |
Abstract: 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. | Simultaneous estimation of two bending vibration frequencies for attitude control of a flexible launch vehicle AM Khoshnood, J Roshanian, AA Jafari, A Khaki-Sedigh Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering | 2009 |
Abstract: 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. | Stability Proof of Gain-Scheduling Controller for Skid-to-Turn Missile Using Kharitonov Theorem MA Sharbafi, A Mohammadinejad, J Roshanian, A SEDIGH JAST | 2009 |
Abstract: 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. | The Effect of Tuning in Multiple-Model Adaptive Controllers: A Case Study Ehsan Peymani, Alireza Fatehi, Ali Khaki Sedigh IFAC Proceedings Volumes | 2009 |
Abstract: 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. | Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki Sedigh Fuzzy Sets and Systems | 2009 |
Abstract: 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. | A Modified Proportional Navigation Guidance for Range Estimation A Moharampour, J Poshtan, A Khaki-Sedigh Iranian Journal of Electrical and Electronic Engineering | 2008 |
Abstract: 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. | A neuro-fuzzy controller for rotary cement kilns Maryam Fallahpour, Alireza Fatehi, Babak N Araabi, Morteza Azizi IFAC Proceedings Volumes | 2008 |
Abstract: 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. | A new approach to compute the cross-Gramian matrix and its application in input-output pairing of linear multivariable plants B Moaveni, A Khaki-Sedigh Journal of Applied Sciences | 2008 |
Abstract: 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. | A QFT/EEAS Design of Multivariable Robust Adaptive Controllers Omid Namaki-Shoushtari, A Khaki Sedigh, B Nadjar Araabi IFAC Proceedings Volumes | 2008 |
Abstract: 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. | Adaptive control of nonlinear in parameters chaotic systems SM Ayati, A Khaki-Sedigh Nonlinear Dyn. Syst. Theory | 2008 |
Abstract: 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. | Decentralised control of MIMO plant using optimal gramian-based pairing Alireza Fatehi International Journal of Automation and Control | 2008 |
Abstract: 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. | Delayed feedback control of time-delayed chaotic systems: Analytical approach at Hopf bifurcation Nastaran Vasegh, Ali Khaki Sedigh Physics Letters A | 2008 |
Abstract: 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. | Design of very thin wide band absorbers using modified local best particle swarm optimization Somayyeh Chamaani, Seyed Abdullah Mirtaheri, Mahdi Aliyari Shooredeli AEU-International Journal of Electronics and Communications | 2008 |
Abstract: 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. | Designing a neuro-fuzzy controller for a vehicle suspension system using feedback error learning SH Sadati, M Aliyari Shooredeli, AD Panah Journal of mechanics and aerospace | 2008 |
Abstract: 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. | Disturbance Rejection in Neural Network Model Predictive Control Alireza Fatehi, Houman Sadjadian, Ali Khaki-Sedigh, Ali Jazayeri IFAC Proceedings Volumes | 2008 |
Abstract: 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. | Flexible structure multiple modeling using irregular self-organizing maps neural network Alireza Fatehi, Kenichi Abe International Journal of Neural Systems | 2008 |
Abstract: 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. | Hybrid control of magnetic levitation system based-on new intelligent sliding mode control M Aliasghary, M Teshnehlab, A Jalilvand, M Aliyari Shoorehdeli, MA Nekoui Journal of Applied sciences | 2008 |
Abstract: 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. | Identification and abnormal condition detection of a cement rotary kiln Iman Makaremi, Alireza Fatehi, Babak N Araabi, Morteza Azizi, Ahmad Cheloian IFAC Proceedings Volumes | 2008 |
Abstract: 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. | Input–output pairing analysis for uncertain multivariable processes Bijan Moaveni, Ali Khaki Sedigh Journal of Process Control | 2008 |
Abstract: … | Integrated vs usual treatment model in first episode—Psychosis among Iranian adolescents J Alaghband-rad, Z Shahrivar, J Mahmoudi-gharaei, V Sharif, H Amini, Y Mottaghipour, M Jalali Roudsari, M Motlagh, F Moharari, F Mousavi, H Shahrokhi, A Sedigh, N Salesian, F Razmjoo Early Intervention in Psychiatry | 2008 |
Abstract: 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. | Lipschitz numbers: a medium for delay estimation Iman Makaremi, Alireza Fatehi, Babak Nadjar Araabi IFAC Proceedings Volumes | 2008 |
Abstract: 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. | Load frequency control in interconnected power system using multi-objective PID controller K Sabahi, A Sharifi, M Aliyari, M Teshnehlab, M Aliasghary Journal of Applied Sciences | 2008 |
Abstract: 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. | Model predictive control of a nonlinear fed-batch fermentation process Ahmad Ashoori, Amir Hosein Ghods, Ali Khaki-Sedigh, Mohammad Reza Bakhtiari Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference on | 2008 |
Abstract: 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. | Model reference adaptive control for a flexible launch vehicle A. Khoshnood, J. Roshanian, A. Khaki-Sedig Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering | 2008 |
Abstract: 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. | Passivity-Based Structured Model Predictive Control with Guaranteed Stability Ghazal Montaseri, Mohammad Javad Yazdanpanah, Ali Khaki-Sedigh 제어로봇시스템학회 국제학술대회 논문집 | 2008 |
Abstract: 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). | Real-Time Level Plant Control Using Improved BELBIC Mojtaba Masoudinejad, Rahman Khorsandi, Alireza Fatehi, Caro Lucas, S Fakhimi Derakhshan, Mohammad Reza Jamali IFAC Proceedings Volumes | 2008 |
Abstract: 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. | SHORT TERM PREDICTION OF AIR POLLUTION USING MLP, GAMMA, ANFIS, AND MIXED TRAINING METHODS BASED ON PSO SHOUREHDELI M ALIARI, M TESHNEHLAB, SEDIGH A KHAKI JOURNAL OF CONTROL | 2008 |
Abstract: 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. | Validity of the iranian version of the structured clinical interview for DSM-IV (SCID-I) in the diagnosis of psychiatric disorders HOMAYOUN AMINI, VANDAD SHARIFI, SM Asadi, MOHAMMAD REZA MOHAMMADI, HOSSEIN KAVIANI, Y Semnani, AMIR SHAABANI, Z SHAHRIVAR, ASHTIANI R DAVARI, SHOUSHTARI M HAKIM, ARSHIA SEDIGH, ROUDSARI M JALALI | 2008 |
Abstract: 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. | A new stabilizing control law with respect to a control Lyapunov function and construction of control Lyapunov function for particular nonaffine nonlinear systems A Shahmansoorian, B Moshiri, A Khaki Sedigh, S Mohammadi Journal of Dynamical and Control Systems | 2007 |
Abstract: 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. | An algorithm for systematic pairing of square and nonsquare MIMO plants using balanced realization interaction measure AR FATEHI JOURNAL OF CONTROL | 2007 |
Abstract: 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. | Anxiety disorders in multiple sclerosis: significance of obsessive-compulsive disorder comorbidity Amir Shabani, Jafar Attari Moghadam, Leily Panaghi, Arshia Seddigh Journal of Research in Medical Sciences | 2007 |
Abstract: 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. | Further Theoretical Results on “Relative Gain Array for Norm-Bounded Uncertain Systems” Bijan Moaveni, Ali Khaki Sedigh Industrial & Engineering Chemistry Research | 2007 |
Abstract: 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. | Improved multivariable quantitative feedback design for tracking error specifications SM Mahdi Alavi, A Khaki-Sedigh, B Labibi, MJ Hayes IET Control Theory & Applications | 2007 |
Abstract: 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. | Input-output pairing for nonlinear multivariable systems Bijan Moaveni, Ali Khaki-Sedigh Journal of applied sciences | 2007 |
Abstract: … | Neural network using genetic algorithms(NN using GA) for solving systems of linear equations and findingthe inversion of a matrix Z Ghassabi, B Moaveni, A Khaki-Sedigh WSEAS Transactions on Computers | 2007 |
Abstract: 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. | POWER SYSTEM VOLTAGE STABILITY ENHANCEMENT BY HIGH SIDE VOLTAGE CONTROL FROUD A AKBARI, HOSSEIN SEYFI, SEDIGH A KHAKI MODARES TECHNICAL AND ENGINEERING | 2007 |
Abstract: 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. | Reconfigurable controller design for linear multivariable plants Bijan Moaveni, Ali Khaki-Sedigh International Journal of Modelling, Identification and Control | 2007 |
Abstract: 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. | Advanced HSVC tuning in multi-machine power systems for loadability improvements A Akbari Foroud, H Seifi, A Khaki Sedigh Electric Power Components and Systems(Taylor & Francis Group) | 2006 |
Abstract: 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. | Modeling hybrid systems with MLD approach and analysis of the model size and complexity H Mahboubi, B Moshiri, A Khaki Seddigh International Journal of Electrical and Computer Engineering | 2006 |
Abstract: 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. | NON-AFFECTIVE ACUTE REMITTING PSYCHOSIS IN IRAN ROOZBEH HOSPITAL, 2002-2004 J ALAGHBANDRAD, M BROUMAND, HOMAYOUN AMINI, V SHARIFI, A OMID, ASHTIANI R DAVARI, A SEDIGH, F MOUMENI, POOR Z AMINI TEHRAN UNIVERSITY MEDICAL JOURNAL (TUMJ) | 2006 |
Abstract: 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. | Observer-based design of set-point tracking adaptive controllers for nonlinear chaotic systems A Khaki-Sedigh, A Yazdanpanah-Goharrizi Chaos, Solitons & Fractals(Pergamon) | 2006 |
Abstract: 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. | Combining Context and Emotional Temporal Difference Learning in Control Engineering J Abdi, F Rashidi, C Lucas, SEDIGH A KHAKI SHARIF: ENGINEERING | 2005 |
Abstract: 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. | Hybrid modeling and optimal control of a two-tank system as a switched system H Mahboubi, B Moshiri, A Khaki Seddigh World Academy of Science, Engineering and Technology | 2005 |
Abstract: 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. | Neural networks approaches for computing the forward kinematics of a redundant parallel manipulator H Sadjadian, HD Taghirad, A Fatehi International Journal of Computational Intelligence | 2005 |
Abstract: 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. | Reconfigurable control system design using eigenstructure assignment: static, dynamic and robust approaches A Esna Ashari*, A Khaki Sedigh, MJ Yazdanpanah International Journal of Control(Taylor & Francis Group) | 2005 |
Abstract: 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. | STOCK PRICE MODELING AND FORECASTING USING STOCHASTIC DIFFERENTIAL EQUATIONS H KHALOUZADEH, SEDIGH A KHAKI TAHGHIGHAT-E-EGHTESADI | 2005 |
Abstract: 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. | Adaptive calculation of Lyapunov exponents from time series observations of chaotic time varying dynamical systems A Khaki-Sedigh, M Ataei, B Lohmann, C Lucas Nonlinear Dynamics and Systems Theory | 2004 |
Abstract: 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. | Control of multivariable systems based on emotional temporal difference learning controller J Abdi, KHALILI GH H FAMIL, K LUCAS, SEDIGH A KHAKI, M FATOURECHI INTERNATIONAL JOURNAL OF ENGINEERING | 2004 |
Abstract: 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. | Design of bearing-only vision-based tracking filters Mohammad Hossein Ferdowski, Parviz Jabehdar Maralani, Ali Khaki Sedigh International Society for Optics and Photonics | 2004 |
Abstract: 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. | Model based method for estimating an attractor dimension from uni/multivariate chaotic time series with application to Bremen climatic dynamics M Ataei, B Lohmann, A Khaki-Sedigh, C Lucas Chaos, Solitons & Fractals(Pergamon) | 2004 |
Abstract: 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. | Multivariable Systems Temporal Difference Emotional Control J Abdi, C Lucas, AK Sedigh, M Fatourechi JOURNAL OF FACULTY OF ENGINEERING (UNIVERSITY OF TEHRAN) | 2004 |
Abstract: 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. | On the approximation of pseudo linear systems by linear time varying systems M Samavat, SEDIGH A KHAKI, SP Banks INTERNATIONAL JOURNAL OF ENGINEERING | 2004 |
Abstract: 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. | Decentralized stabilization of large-scale systems via state-feedback and using descriptor systems Batool Labibi, Boris Lohmann, Ali Khaki Sedigh, Parviz Jabedar Maralani IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans | 2003 |
Abstract: 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. | Estimating the lyapunov exponents of chaotic time series based on polynomial modelling M Ataei, A Khaki-Sedigh, B Lohmann IFAC Proceedings Volumes | 2003 |
Abstract: 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. | Evaluating methods of the share price forecastability in Tehran stock exchange H KHALOUZADEH, SEDIGH A KHAKI MODARRES HUMAN SCIENCES | 2003 |
Abstract: 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. | Identification of the dynamics of the google’s ranking algorithm A Khaki Sedigh, Mehdi Roudaki 13th IFAC Symposium on system identification | 2003 |
Abstract: 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. | Model Based Method for Determining the Minimum Embedding Dimension from Chaotic Time Series-Univariate and Multivariate Cases M Ataei, B Lohmann, A Khaki-Sedigh, C Lucas NONLINEAR PHENOMENA IN COMPLEX SYSTEMS-MINSK- | 2003 |
Abstract: 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. | ON THE APPROXIMATION OF PSEUDO LINEAR SYSTEMS BY LINEAR TIME VARYING SYSTEMS (RESEARCH NOTE) M Samavat, A Khaki Sedigh, SP Banks International Journal of Engineering-Transactions A: Basics | 2003 |
Abstract: 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. | Robust decentralized control of large-scale systems via H∞ theory and using descriptor system representation Batool Labibi, Boris Lohmann, A Khaki Sedigh, P Jabedar Maralani International Journal of Systems Science | 2003 |
Abstract: 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. | Robust decentralized stabilization of large-scale systems via eigenstructure assignment Batool Labibi, Boris Lohmann, Ali Khaki Sedigh, Parviz Jabedar Maralani International Journal of Systems Science | 2003 |
Abstract: 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 | ROBUST TRACKING BY USING MEASURE THEORY A ZAREA, SEDIGH A KHAKI, KAMYAD A VAHIDIAN NASHRIYYAH-I MUHANDESI-I BARQ VA MUHANDESI-I KAMPYUTAR-I IRAN (PERSIAN) | 2003 |
Abstract: 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. | Solving weighted mixed sensitivity H∞ problem by decentralized control feedback via modifying weighting functions and using descriptor system representation IFAC Proceedings Volumes | 2003 |
Abstract: 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. | THE USING OF MEASURE THEORY IN MINIMAL TIME OF OPTIMAL CONTROL PROBLEMS A ZAREA, SEDIGH A KHAKI, AV KAMYAD INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE)(INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE)(PERSIAN) | 2003 |
Abstract: 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. | Decentralised quantitative feedback design of large-scale systems B Labibi, B Lohmann, A Khaki Sedigh, P Jabedar Maralani IFAC Proceedings Volumes | 2002 |
Abstract: 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. | Output feedback decentralized control of large-scale systems using weighted sensitivity functions minimization Batool Labibi, Boris Lohmann, A Khaki Sedigh, P Jabedar Maralani Systems & control letters | 2002 |
Abstract: 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. | Design of decentralized high-gain error-actuated controllers for large-scale systems B Labibi, B Lohmann, A Khaki Sedigh, P Jabedar Maralani International Journal of Modelling and Simulation | 2001 |
Abstract: 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. | Design of static linear multivariable output feedback controllers using random optimization techniques Ali Khaki-Sedigh, Yazdan Bavafa-Toosi Journal of Intelligent & Fuzzy Systems | 2001 |
Abstract: 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. | Leveling and gyrocompassing of stable platforms using neural networks SEDIGH A KHAKI, SARVI M NASIRI IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION B-ENGINEERING | 2001 |
Abstract: 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. | Genetic Methodology for Linear Output Feedback Control Law Design Y Bavafa-Toosi, A Khaki-Sedigh Progress in Simulation, Modeling, Analysis and Synthesis of Modern Electrical and Electronics Devices and Systems | 2000 |
Abstract: 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. | Optimal design of robust quantitative feedback controllers using random optimization techniques A Khaki Sedigh, Caro Lucas International Journal of Systems Science | 2000 |
Abstract: 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. | Sufficient condition for stability of decentralised control B Labibi, B Lohmann, A Khaki Sedigh, P Jabedar Maralani Electronics Letters | 2000 |
Abstract: 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. | Controller design using, μ-synthesis for static VAR compensator to enhance the voltage profile for remote induction motor loads M Abedi, SA Taher, AK Sedigh, H Seifi Electric power systems research | 1998 |
Abstract: 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. | Design and Implementation of a Predictive Fuzzy Controller for an Industrial Furnace AK Sedigh, N Afshar, M Afazeli IFAC Proceedings Volumes | 1997 |
Abstract: 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. | Singular perturbation analysis of the step-response matrices of a class of linear multivariable systems B Porter, A Khaki-Sedigh International journal of systems science | 1997 |
Abstract: 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. | Input-output pairing using balanced realisations [multivariable plants] A Khaki-Sedigh, A Shahmansourian Electronics Letters | 1996 |
Abstract: 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. | Design of tunable digital set-point tracking controllers for linear multivariable type-one plants B Porter, A Khaki-Sedigh International Journal of Systems Science | 1990 |
Abstract: 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. | Robustness properties of tunable digital set-point tracking PID controllers for linear multivariable plants B Porter, A Khaki-Sedigh International Journal of Control | 1989 |
Abstract: 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. | Design of Robust Adaptive Digital Set-Point Tracking P1 Controllers for Nonminimum-Phase Multivariable Plants B Porter, A Khaki-Sedigh IFAC Proceedings Volumes | 1988 |
Abstract: 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. | Design of robust adaptive digital setpoint tracking Pl controllers incorporating recursive step-response matrix identifiers for gas turbines B Porter, A Khaki-Sedigh Transactions of the Institute of Measurement and Control | 1988 |
List of Journal Papers