Articles by Year

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.

Nowadays, Human Immunodeficiency Virus (HIV) does not have a certain cure and current treatment can only control the virus. In recent years, highly active antiretroviral therapy (HAART) is used for the treatment. Since HAART has also undesirable side effects, there is a trade-off in its dosage. To control the illness and minimize the side effects, a multi-objective problem can be solved for a treatment plan. Therefore, this paper presents multi-objective treatment strategies for HIV. Two cost functions are defined. One for drug dosage treatment and one for the concentration of CD4. The multi-objective problem solved by NSGA-II and NSIWO, to produce optimal control inputs. The Pareto frontier suggested optimal strategies which the regime is selected depending on the circumstance. The performance of the NSIWO and NSGA-II to find the Pareto front for this multi-objective problem is investigated.

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.

According to the repeated-game theory, the continual interactions of electricity producers can increase the probability of collusion between these firms. As a result, the market equilibrium will be established based on tacit collusion. In this paper, the model of collusion in a power pool market is presented in the form of a mathematical program with equilibrium constraint (MPEC). This program takes into account the transmission network and production capacity constraints of the power plant, and the uncertainty property caused by the demand shock. The uncertainty is shown by a random variable in this scenario. Assuming that the probability density function of the random variable is general knowledge for all stakeholders of the electricity market, and the risk tolerance is zero, the collusion resulting from the strategy due to uncertainty is shown as the market equilibrium. Finally, it is concluded that the collusive expected profits in anticipation of a high demand market are higher than a low demand market.

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.

In this paper, a recurrent neural network coupled with Kalman filter is proposed to identify dynamic terms of robotic manipulator. By cooperating some inherent characteristics of robot, this network has the capability to individually identify nonlinear terms using Weighted Augmentation Error (WAE). To present the infrastructure of architecture, an adaptive scheme based on the conventional Back Propagation (BP) is firstly driven using the Gradient Descent (GD) method. Additionally, a stable adaptive updating rule is extracted from the discrete time Lyapunov candidate as an approach for the general nonlinear system identification. Then, this approach is applied to the predefined network. To experimentally validate the computational efficiency and control applicability of the proposed method, Adaptive Neural Network Based Inverse Dynamic Control (ANN-Based-IDC) is employed on a laboratory-scaled twin-rotor CE-150 helicopter. This experiment illustrates enhancement of steady-state performance from 2-to-3 times more in compared with simple PID. Moreover, disturbance rejection and robustness tests admit capability of the method for online dynamic identification in the presence of output and dynamic perturbation.

In the recent years, artificial neural network have been used to improvement of system identification. The performance of neural network directly depends on the hidden layer, which include weights and activation functions of the network. In addition Genetic Algorithms are used to learn of neural network as a type of evolutionary computing algorithms. In this paper, the structure of hidden layers and weights are modified by using biological neuron model of Izhikevich. These two methods, Genetic Algorithms and biological model of neuron, merge together for designing a novel structure.

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.

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.

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.

In this paper, Linear Quadratic Gaussian (LQG) controller extended to a class of nonlinear systems based on subspace matrices using bilinear model. LQG controller design based on subspace matrices provides directly from system input output data. Therefore it is more useful for systems that their models are not available. Since the most practical systems are nonlinear, LQG controller design based on linear subspace model is reflected to a weak control performance or even instability. To overcome this problem, LQG controller design based on bilinear subspace model is presented. Simulation results and comparison studies are provided to show the effectiveness of proposed method.

This paper 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.

In This study, we present a new sensor fault detection approach based on nonlinear parity technique in presence of sensor noise. Conventionally analytical redundancy (AR) was used to fault detection and isolation in linear systems. The proposed parity space approach with nonlinear analytical redundancy (NLAR) technique can be applied to detect sensor fault in the nonlinear affine systems with mentioned class. The proposed approach will be implemented in pH neutralization system. At the end nonlinear fault detection and identification algorithm will be successfully implemented, examined and reported.

The topic of control performance assessment techniques has drawn lots of attentions and many performance assessment indices have been proposed. These indices are focused on certain malfunctions. Fallacious decision would result in if it is based on individual indices. Therefore, fusion of different indices can improve the assessment accuracy. In this paper, Bayesian and Dempster-Shafer theory are used individually to establish decision fusion strategy and tackle the performance assessment challenge of heavy duty gas turbine. To study the uncertainty effect on these methods, they are applied to the well-known Rowen model of gas turbine. The results illustrate the effectiveness of the proposed performance assessment method of the gas turbine model on the one hand, and the superiority of the Bayesian method when the uncertainty is low and that of Dempster-Shafer theory in the presence of uncertainty, on the other hand.

In this study, a novel fuzzy unknown input observer for robust fault estimation scheme is developed when both faults and unknown input are considered. The proposed scheme includes component fault with nonlinear distribution matrix in state equation, unknown input signal in state and output equations. After that, Takagi-Sugeno (T-S) model is used to create multiple models. While T-S model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear system will be included. Two set of observers are considered, the first one is extended fuzzy unknown input observer (EFUIO) and the other one is fuzzy sliding mode observer (FSMO). The approach decoupled the faulty subsystem from the rest of the system through a series of linear transformations. Then, the objective is to design EFUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method. Unknown input is removed; meanwhile, FSMO is designed for faulty subsystem to guarantee estimation of fault. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities (LMIs). Finally, a simulation study on an electromagnetic suspension system (EMS) is presented to demonstrate the performance of the results compared with a pure SMO.

This paper 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.

State estimation and fusion is studied using Kalman filter (KF) when a slow-rate integrated measurement is available. Integrated measurement is common in industrial processes, when a sample of material is gradually collected over some period of time and then sent to a laboratory for analysis. In this case, the laboratory measurement will reflect the material properties that have been integrated over the sampling period. The goal is to estimate the fast-rate value of states that evolve with time. A modified KF is proposed to execute state estimation using a slow-rate integrated measurement. Fusion of the slow-rate state estimate and other fast-rate measurements can improve the final state estimation of the process. The performance of the proposed method is demonstrated through both simulation and experimental study in a laboratory scale hybrid tank pilot plant.

In dealing with model predictive controllers (MPC), controller tuning is a key design step. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a recently proposed analytical MPC tuning approach based on low order models. The performance of such methods deteriorates in dealing with unknown or time varying parameter plants. To overcome this problem, adaptive MPC tuning strategies are practical alternatives. The adaptive MPC tuning approach proposed in this paper is based on on-line identification and analytical tuning formulas. Simulation results are used to show the effectiveness of the proposed methodology.

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.

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.

In this study, a novel fuzzy robust fault estimation scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes component fault with a nonlinear distribution matrix; as a result, the Takagi–Sugeno model is used to create multiple models. While the Takagi–Sugeno model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear systems will be considered. This paper presents the problem of robust fault estimation based on fuzzy nonlinear observers, the first one is a fuzzy unknown input observer and the other one is a fuzzy sliding mode observer. The approach decoupled the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design a fuzzy unknown input observer guaranteeing the asymptotic stability of the error dynamic using the Lyapunov method and completely removing disturbances; meanwhile, a fuzzy sliding mode observer is designed for a faulty subsystem to generate an estimation of fault based on a quadratic Lyapunov function and some matrices inequality convexification techniques. The sliding motion affects only the faulty subsystem through a novel reduced order fuzzy sliding mode observer; meanwhile, all disturbances are completely removed by fuzzy unknown input observer. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities. Thus, an exact fault estimator is determined on the basis of linear matrix inequality conditions while the estimation fault is completely insensitive to the disturbance. Finally, a simulation study on an electromagnetic suspension system is presented to demonstrate the g performance of the results compared with a pure sliding mode observer.

This paper 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.

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.

Optimal solution for nonlinear identification problem in the presence of non-Gaussian distribution measurement and process noises is generally not analytically tractable. Particle filters, known as sequential Monte Carlo method (SMC), is a suboptimal solution of recursive Bayesian approach which can provide robust unbiased estimation of nonlinear non-Gaussian problem with desire precision. On the other hand, Hunt-Crossley is a widespread nonlinear model for modeling telesurgeries environment. Hence, in this paper, particle filter is proposed to capture most of the nonlinearities in telesergerie environment model. An online Bayesian framework with conventional Monte Carlo method is employed to filter and predict position and force signals of environment at slave side respectively to achieve transparent and stable bilateral teleoperation simultaneously. Simulation results illustrate effectiveness of the algorithm by comparing the estimation and tracking errors of sampling importance resampling (SIR) with extended Kalman filter.

Gaussian process (GP) regression is a fully probabilistic method for performing non-linear regression. In a Bayesian framework, regression models can be made robust by using heavy-tailed distributions instead of using normal distribution for modeling noise. This work focuses on estimation of parameters for robust GP regression. In literature, these are learned by maximizing the approximate marginal likelihood of data. However, gradient-based optimization algorithms which are used for this purpose can be unstable or may require tuning. In this work, an EM algorithm based approach is derived and implemented to infer the parameters. The pros and cons of the two approaches are analyzed. The advantage of EM algorithm lies in its ease of implementation and theoretical guarantees of numerical stability and convergence while its prediction performance is still comparable to gradient-based approaches. In some cases EM algorithm may be slow to converge. To circumvent this issue a faster EM based approach known as Expectation Conjugate Gradient (ECG) is implemented on robust GP regression. Finally, the proposed EM approach to robust GP regression is validated using an industrial data set.

In this paper, particle dynamics and stability analysis of gravitational search algorithm (GSA) are investigated. The GSA is a swarm optimization algorithm which is inspired by the Newtonian laws of gravity and motion. Previously, the convergence analysis of the GSA and improved GSA algorithms were presented to demonstrate each particle converges. In this study, the stability of the particle dynamics using Lyapunov stability theorem and the system dynamics concept is analyzed. Sufficient conditions of stability analysis are investigated and utilized for adapting parameters of the GSA. The modified algorithm based on stability analysis is compared with the standard GSA, PSO, RGA, and two methods of improved GSA in terms of average, median, and standard deviation of best-so-far solutions. Simulation results demonstrate the validity and feasibility of the proposed modified GSA. In solving the optimization problem of various nonlinear functions, the high performance is achieved.

In this paper, a novel architecture in multilayer perceptron (MLP) neural network with flexible activation function and adaptive learning rate is presented for a data-driven identification of robot dynamics. It is assumed that the measurement of robot end-effector position, velocity and acceleration are available corrupted by Gaussian noise. Since some general property of robot dynamics are included in the proposed structure as well as optimization indices, this structure is envisaged having good performance in confronting with uncertainty in measurements. The main contribution of this paper is to propose a transparent neural network structure for identification of dynamic terms by introducing a gray-box identifier. Simulation results on 2-DOF serial manipulator reveal the accuracy of the method. Finally, experimental results on a laboratory-scaled twin rotor CE 150 helicopter indicate the applicability of the proposed method.

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.

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems.

In this study, a new strategy for fault detection and isolation is presented. This strategy is based on the design of a Lüneburg observer which is implemented via pole placement using linear matrix inequalities. Two residuals are formulated based on the state estimation error in order to be utilized in detecting and isolating faults happened on the system. Fault detection problem solves by changes occur in the residual value and fault isolation is done through determining threshold on residuals according to system behavior in faulty condition. The procedure performs in four simulations steps in which there are certain numbers of faults happen in the system in each step. This method is validated in simulation on a quadruple tank process while each faulty condition is considered as a leak at the bottom of a tank in the process. This can lead to an undesirable flow of liquid out of the tank which results to a decrease in tank's level. The simulation results represented in the paper shows the applicability of this strategy.

Unmanned Aerial Vehicles (UAVs) pose a multi-input and multi-output (MIMO) dynamic structure, making their simultaneous guidance and control too complicated to be maintained via conventional scalar controllers. In this paper, a multivariable optimal controller is introduced based upon LQG\LTR design approach to effectively control the UAV attitude in the presence of noise and disturbance. The regulator design problem is solved by generating an optimal state estimate using a Kalman filter. A loop transfer recovery (LTR) procedure is developed to allow good recovery of the full state feedback properties, enhancing stability and performance robustness. This scheme facilitates proper integration of system's gain at different frequencies in order to provide optimal bandwidth and yet weakening the noise effects. The corresponding rate of return gains is set in frequency-domain to achieve robust performance characteristics. A set of tests is conducted on an UAV simulation case study to explore its performance under different scenarios. The results clearly demonstrate well performances in the face of the induced noise and couplings between the system channels.

This paper considers the problem of controlling coupled chaotic maps. Coupled chaotic maps or multichaotic subsystems are complex dynamical systems that consist of several chaotic sub-systems with interactions. The OGY methodology is extended to deal with the control of such systems. It is shown that the decentralized control design scheme in which the individual controllers share no information is not generally able to control multichaotic systems. Simulation results are used to support the main conclusions of the paper.

In this paper, the problem of decentralized model reference adaptive control (MRAC) for a class of large scale systems with time varying delay in interconnected term and input and state delays is studied. To compensate the effect of input delay indirectly, a Smith predictor built on. To handle the effects of the time delays in input, the adaptive controller part includes two auxiliary dynamic filters with time varying gains. Under a usual assumption that the interconnections are assumed to be Lipschitz in its variables and uniformly in time with unknown Lipschitz gains, the difficulties from unknown interconnections are dealt. A generalized error is defined and by a suitable Lyapunov function, an adaptive controller is designed to stabilize it. Decentralized adaptive feedback controller can render the generalized error system uniformly ultimately bounded stable is designed. Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed design techniques.

The 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.

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.

 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.

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.

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.

In this study, seismic attributes have been used to estimate well logs in one of the Iranian petroleum reservoirs. Three static methods have been evaluated: the linear model, the multilayer perceptron (MLP) and the radial basis function (RBF). For linear case, the selection of appropriate attributes was determined by forward selection and for nonlinear one, the selection was based on the genetic algorithm (GA) result. Parameters of nonlinear models were determined by cross-validation and then well logs were estimated. By comparing estimated and actual logs, RBF has the best performance with least training error. Since well logs contain high frequency content, so localized networks such as RBF has better performance than MLP through the study data set.

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.

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.

An important feature in dynamic systems that model behavior in human society is the role of expectations formed by individual agents within such systems. Unlike physical system in engineering, social systems are inhabited by sentient decision makers that react to their environment and form expectations about future events and the decisions of other agents in the society. As a consequence, conventional tools used in engineering may not apply. However, a large body of contributions in the engineering literature to the field of intelligent systems may still be useful for the analysis of expectational dynamic social systems. This paper adapts an emerging literature on agentbased computational economics, exemplified in (LeBaron and Tesfatsion (2008); Oeffner (2008); Tesfatsion (2002 )), to the issue of deriving from-the-ground-up formulas of expectations useful for macro-economic analysis. It joins established optimization methods in economics with results in multi-agent predictive control from the engineering literature summarized in (Cao, Yu, Ren and Chen (2013); Maestre, Muñoz de la Peña and Camacho (2011 )). The benefit to this approach is that the resulting dynamic structures under individual optimization are causal and easily solved, unlike typical non-causal rational expectations models.

This paper presents a 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.

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.

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.

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.

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.

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.

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).

It is not possible to directly measure the total mass of gas and liquid in the annulus and geological properties of the reservoir during petroleum exploration and production drilling. Therefore, these parameters and states must be estimated by online estimators with proper measurements. This paper describes a nonlinear Moving Horizon Observer to estimate the annular mass of gas and liquid, and production constants of gas and liquid from the reservoir into the well during Under-Balanced Drilling with measuring the choke pressure and the bottom-hole pressure. This observer algorithm based on a low-order lumped model is evaluated against Joint Unscented Kalman filter for two different simulations with low and high measurement noise covariance. The results show that both algorithms are capable of identifying the production constants of gas and liquid from the reservoir into the well, while the nonlinear Moving Horizon Observer achieves better performance than the Unscented Kalman filter.

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.

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.

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.

Modern systems are required to guarantee a high degree of safety and self-diagnostics capabilities. This paper investigates the problem of state fault diagnosis in nonlinear systems with modeling uncertainties. In contrast with common literature, the fault diagnosis scheme is proposed in discrete time domain. This property relaxes the risk of stability and performance degradation in deriving discrete equivalent of continuous methods. An estimator is designed in order to generate residual signal by utilizing a proper nonlinear state transformation. A robust compensator term is implemented in estimator to decrease effect of modeling uncertainties and approximation error on residual signal. When the residual signal is exceeded detection threshold, an on-line fault approximator is turned on and trained by appropriate parameter update law. An extra term is considered in update rule to overcome the need of persistency of excitation (PE). The implement of all robust compensator term, PE relaxing term and proper parameter adaption law improve the accuracy of fault reconstruction. The result would be obviously vital in tolerant and time-life prediction stages after fault diagnosis.

A relatively simple and exact solution of a control allocation algorithm with low computational cost can greatly influence a multivariable system performance. In this paper the pseudo inverse approach is used to achieve the exact answer. Then, the solution is modified by the null space of control matrix in order to satisfy the constraints. Therefore, we could hold the simplicity and exactness of the pseudo inverse approach and remove its deficiency by the proposed methodology. Furthermore, a simulation is provided to show the main characteristics of the proposed method and its superiority.

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.

This paper presents a constrained model predictive control scheme for regulation of the annular pressure in a well during managed pressure drilling from a floating rig subject to heave motion. The results show that closed-loop simulation without disturbance has a fast regulation response and without any overshoot. The robustness of controller to deal with heave disturbances is investigated. The constrained MPC shows good disturbance rejection capabilities. The simulation results show that this controller has better performance than a PID controller and is also capable of handling constraints of the system with the heave disturbance.

An adaptive fault tolerant control systems are vital in many industrial systems. Redundancy is a practical approach to decrease the effects of faults in systems. Redundancy in actuators can also increase system reliability and flexibility. This paper proposes a fuzzy control allocation method that can allocate control signal among actuators to increase reliability and maneuverability in healthy conditions and tolerating faults in faulty conditions. Using fuzzy logic is an intelligent way to adaptively change the gains of control allocation in different operating conditions.

Control of pH neutralization process has always been one of challenging problem in process control. The method presented here to control this process is the fuzzy identification of systems using Wiener model, and then multiplying the measured signal by the inverse of the nonlinear part of model. Therefore, we can design a linear controller for this new augmented system. This strategy is implemented in a generalized predictive control. One of the advantages of this control structure is consideration of explicit constraint in control of systems which also included in proposed fuzzy predictive control. At the end, the proposed method is tested on the model of a pH neutralization process.

In this study, first by using the collected real data from a 10000 cubic-meter Qazvin-kowsar water supply reservoir is modeled by nonlinear output error (NOE) structure, then a neural nonlinear controller based on the MLP neural network according to created model is designed in order to control the tank water level. The operation of the proposed controller is compared by a PID controller which its coefficients is optimized by genetic algorithm. Results of the simulation indicates that the neural nonlinear controller has a better function than the PID controller, and also this controller is able to control the level water of the tank appropriately regardless the consumer profile in all conditions even in consumer picks.

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.

This study proposes a novel chaotic anti-control for flexible joint system. The proposed controller is composed of a Lyapunov rule-based fuzzy control and chaotic anti-control for target tracking of the flexible joint manipulator. Chaotic signal is used to study the effect of anti-control to reduce the deflection of flexible joint system and control signal energy. For this purposes the flexible joint has been synchronized with chaotic Lorenz system. In this study on of the Lorenz parameters is changed to analysis the effect of chaotic signals. The results of the proposed approach shows in terms of level of vibration reduction and energy consumption of control signal, we could find an optimum point based on value of Lorenz system parameter. Finally, the efficacy of the proposed method and results of existence of different nonlinearity behavior is validated through experiments on QUANSER's flexible-joint manipulator.

In this study interval type-2 fuzzy systems with non-singleton type-2 fuzzifire are used for identification and modeling nonlinear systems having noise with changing domain for fault detection purpose. The main idea in this fault detection method is to serve an upper bound and a lower bound as a confidence bound for system output that obtained from the interval type-2 fuzzy system. If we haven't precise information about mean and variance of noise, then non-singleton type-2 fuzzifire is usable. This fuzzifire improves performance of fault detection confidence bound. In the end of this paper a well-known benchmark two-tank system has been used for representing the advantages of proposed fault detection method.

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.

In this paper, obtaining of maximum active and reactive output power for wind turbines equipped with a double fed induction generator using stator-flux-oriented vector control based on novel multivariable input output linearization sliding mode control presented. The main control problem is the estimation of maximum power operating points of wind turbine under stochastic wind velocity profiles and tracking them using conventional offline and innovative adaptive online method. In this control strategy the wind speed and consequent aerodynamics torque is considered as the disturbance. Results under different operating conditions show the superior performance of the proposed online input-output linearization sliding mode technique.

This paper concerns application of data-derived approaches for analyzing and monitoring chemical process instruments, extracting product information, and designing estimation models for primary process variables, or difficult to measure in real-time variables. Modeling of process with an optimized classical neural network, the multi-layer perceptron (MLP) is discussed. Tennessee Eastman Process, a well-known plant wide process benchmark, is applied to validate the proposed approach. Investigations and several algorithms as step response test, Lipschitz number method and forward selection are used. The main advancement introduced here is that a hierarchical level responsible strategy is applied for selection of input variables and respective efficient time delays to attain the highest possible prediction accuracy of the neural network model for industrial process identification.

It has been known that, real right half plane (RHP) zeros imply serious limitations on the performance of nonminimum phase systems. Feedback cannot remove these limitations, mainly because RHP zeros cannot be cancelled by unstable poles of the controller since such a cancellation leads to internal instability. Hence, the idea of using fractional order systems in partial cancellation of the RHP zeros without leading to internal instability is studied. In this paper, the partial cancellation of RHP zeros with RHP poles is proposed using the fractional calculus approach. It is shown that undershoot and settling time of the compensated system is improved. Using suitable optimum criterion, it is shown that the performance of closed loop system can be relatively improved. Simulation results are used to show the effectiveness of the proposed methodology.

To improve the performance of a robust control, in presence of internal or external disturbance and uncertainties, to make a smooth tracking and elimination of high frequency disturbances especially in accurate systems with minimum power consumption an integration of robust optimal controller considered. Here, derivation and implementation of the proposed controller based on the combination of and controllers to use their characteristics against unknown disturbances is considered. The proposed controller was implemented on a 3 axis gyro-stabilized MIMO platform. The results which express the control designer desires, compared to the implemented NLPID and a single controller on the same system.

In this paper, for a class of linear systems with unknown parameters, a direct model reference adaptive control scheme in output feedback form has been presented, which assures stable adaptation in the presence of input saturation. Also, under certain assumptions one can guarantee that the adaptive control signal will avoid input saturation. In addition, by considering that the error model is in a parametric model form, robust adaptive control is used to improve robustness of systems in the presence of bounded disturbances. This is achieved by using the a-modification method. Simulation of an output feedback system with relative degree 2 verifies the results given in the paper.

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.

This study applies technique PDC (parallel distributed compensation) for speed control of a Digital Servo System. PDC method is based on nonlinear Takagi-Sugeno (TS) fuzzy model. Also in this study Neural Adaptive is used for velocity control and identification of a Digital Servo System. It is shown that these techniques can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by experimental and computer simulation. The controllers which introduced have big range for control the system. We compare PDC controller with Neural Adaptive controller results and PID controller.

Nonlinear behavior and disturbance sensitivity of the pH processes causes them to be known as an appropriate test bench for advanced controllers. Because of special behavior and varying parameters of pH processes, Multiple Model Predictive Controllers (MMPC) outperform other controllers from both regulation and disturbance rejection points of views. Two new supervisory methods based on prediction error and fuzzy weighting for MMPC are presented. Better regulation in special condition and most excellent disturbance rejection in comparison to other MMPC methods are achieved.

This 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.

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.

In this paper, a new approach to the problem of stabilizing a chaotic system is presented. In this regard, stabilization is done by sustaining chaotic properties of the system. Sustaining the chaotic properties has been mentioned to be of importance in some areas such as biological systems. The problem of stabilizing a chaotic singularly perturbed system will be addressed and a solution will be proposed based on the OGY (Ott, Grebogi and Yorke) methodology. For the OGY control, Poincare section of the system is defined on its slow manifold. The multi-time scale property of the singularly perturbed system is exploited to control the Poincare map with the slow scale time. Slow scale time is adaptively estimated using a parameter estimation technique. Control with slow time scale circumvents the need to observe the states. With this strategy, the system remains chaotic and chaos identification is possible with online calculation of lyapunov exponents. Using this strategy on ecological system improves their control in three aspects. First that for ecological systems sustaining the dynamical property is important to survival of them. Second it removes the necessity of insertion of control action in each sample time. And third it introduces the sufficient time for census.

In this paper input-output pairing is done based on concept of energy. Parseval theorem and cross-covariance samples of input-output are used for estimation of energy. After approximating interaction energy between input and output of the plant, input-output pairing is fulfilled. Through examples, it is illustrated that proposed method is appropriate for input-output pairing. The result is compared with Effective Relative Energy Array (EREA) as another energy based approach for input-output pairing.

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.

The idea that chaos could be a useful tool for analyze nonlinear systems considered in this paper and for the first time the two time scale property of singularly perturbed systems is analyzed on chaotic attractor. The general idea introduced here is that the chaotic systems have orderly strange attractors in phase space and this orderly of the chaotic systems in subscription with other classes of systems can be used in analyses. Here the singularly perturbed systems are subscripted with chaotic systems. Two time scale property of system is addressed. Orderly of the chaotic attractor is used to analyze two time scale behavior in phase plane.

Breast cancer is the cause of the most common cancer death in women. Early detection of the breast cancer is an effective method to reduce mortality. Fuzzy Neural Networks (FNN) comprises an integration of the merits of neural and fuzzy approaches, enabling one to build more intelligent decision-making systems. But increasing the number of inputs causes exponential growth in the number of parameters in Fuzzy Neural Networks (FNN) and computational complexity increases accordingly. This phenomenon is named as “curse of dimensionality”. The Hierarchical Fuzzy Neural Network (HFNN) and the Fuzzy Gaussian Potential Neural Network (FGPNN) are utilized to deal this problem. In this study, the HFNN and FGPNN by using new training algorithm, are applied to the Wisconsin Breast Cancer Database to classify breast cancer into two groups; benign and malignant lesions. The HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. It can use fewer rules and parameters to model nonlinear system. Moreover, the FGPNN consists of Gaussian Potential Function (GPF) used in the antecedent as the membership function. When the number of inputs increases in FGPNN, the number of fuzzy rules does not increase. The performance of HFNN and FGPNN are evaluated and compared with FNN. Simulation results show the effectiveness of these methods even with less rules and parameters in performance result. These methods maintain the accuracy of original fuzzy neural system and have high interpretability by human in diagnosis of breast cancer.

This paper proposes the chaos control and the 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.

A novel structure of fuzzy logic controller is presented for trajectory tracking and vibration control of a flexible joint manipulator. The rule base of fuzzy controller is divided into two sections. Each section includes two variables. The variables of first section are the error of tip angular position and the error of deflection angle, while the variables of second section are derivatives of mentioned errors. Using these structures, it would be possible to reduce the number of rules. Advantages of proposed fuzzy logic are low computational complexity, high interpretability of rules, and convenience in fuzzy controller. Implementing of the fuzzy logic controller on Quanser flexible joint reveals efficiency of proposed controller. To show the efficiency of this method, the results are compared with LQR method. In this paper, experimental validation of proposed method is presented.

In 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.

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.

In this paper Fault Detection and Isolation (FDI) is shown as a pattern classification problem which can be solved using clustering techniques. Gath-Geva clustering (GGC) is exploited as optimal form by a performance assessment rule for fault detection, while multistage Gath-Geva clustering is employed for the intent of fault isolation. Furthermore since Visbreaker unit is a large scale process, a novel hybrid method on the basis of Principle Component Analysis and Genetic Algorithm optimization was also proposed in order to cope with the curse of dimensionality and complexity of computation problems. There are two main percentile criteria for validation of fault detection namely specificity and sensitivity. Evaluation of fault isolation has been depicted in confusion matrix. For analysis and visualization of the correlated high dimensional data, PCA maps the data point into lower dimensional space. The proposed FDI approaches have been evaluated through experimental Visbreaker process unit data collected in oil refinery.

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.

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.

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.

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.

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.

A combined approach for bumpless transfer multiple model predictive control (Multiple MPC) is proposed based on the Lyapunov function. State-space representation is used to design the controllers and the Lyapunov approach is employed to ensure closed loop stability. The proposed method uses both an intermediate controller and a bumpless mechanism in a unified configuration based on the stability analysis. Previous works on the bumpless multiple MPC design do not ensure closed loop stability, while the mechanism presented in this paper ensures both closed loop stability and applicable control performance for industrial processes. Finally, efficiency of the proposed method is validated by simulation results on non-isothermal continuous stirred-tank reactor (CSTR) system.

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.

This paper proposes the modified projective synchronization for heavy symmetric dissipative gyroscope systems via backstepping control. Because of the nonlinear terms of the gyroscope system, the system exhibits complex and chaotic motions. Using the backstepping control technique, control laws are established which guarantees the hybrid projective synchronization including synchronization, anti-synchronization and projective synchronization. By Lyapunov stability theory, control laws are proposed to ensure the stability of the controlled closed-loop. Numerical simulations are presented to verify the proposed synchronization approach. This paper demonstrates that synchronization and anti- synchronization can coexist in dissipative gyroscope systems via nonlinear control.

This paper 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).

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An output feedback model reference adaptive controller is developed for a class of linear systems with multiple unknown time-varying state delays and in the presence of actuator failures. The adaptive controller is designed based on SPR-Lyapunov approach and is robust with respect to multiple unknown time-varying plant delays and to an external disturbance with unknown bounds. Closed-loop system stability and asymptotic output tracking are proved using suitable Lyapunov-Krasovskii functional and Simulation results are provided to demonstrate the effectiveness of the proposed controller.

In this paper, a new 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.

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.

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.

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.

Based on the Gas Path Analysis (GPA) method, nonlinear estimation and fuzzy classification theories, a comprehensive fault diagnosis system has been developed for an industrial Gas Turbine (GT). The hybrid method consists of two parts, in the first part noisy sensor output changes are translated to changes in the health parameters using an Extended Kalman Filter (EKF). In the second part the outputs of the EKF are used as the inputs of a fuzzy system. This system can isolate and evaluate the physical faults based on the predetermined rules obtained mostly from experimental data and aerothermodynamical simulations. The ratios of changes in different health parameters due to different faults and also the areas in the compressor most affected by these faults are the key factors for developing the rules. The Fuzzy Inference System (FIS) gives the fault locations in the compressor or turbine. Also, operator-friendly suggestions for the time of the compressor washing or components repair are provided. This leads to a hybrid fault detection and isolation solution for the GT, and with pre-filtering the data before use as input of fuzzy inference system, the accuracy of the fault diagnosis system is improved. Nonlinear simulation, estimation and classification results are provided to show the effectiveness of the proposed methodology.

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.

This paper proposes a novel architecture for bilateral teleoperation with a master and slave nonlinear robotic systems under constant communication delays. We basically extend the passivity based coordination architecture to improve position and force tracking and consequently transparency in the face of offset in initial conditions, environmental contacts and unknown parameters such as friction coefficient. This structure provides robust stability against constant delay and guarantee position and force tracking. The proposed controller employ a stable neural network in each side to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitation of adaptive control and guarantee good performance. An adaptation algorithm is developed to train the NN controller in order to stabilize the whole system. Furthermore, it is demonstrate that the tracking error of desired trajectory and NN weights are bounded. Simulation results show that NN controller tracking performance is superior to conventional coordination controller tracking performance.

In this note, an adaptive observer is considered for simultaneous estimation of the states and unknown parameters of linear stationary systems with faulty measurements. Since entries of system matrices are functions of only a few parameters, it is enough to tune those parameters, instead of adapting all entries. This leads to reduced adaptation laws. Moreover, the problem of measurement offset and gain faults are also considered. The stability and convergence of the proposed adaptive observer is investigated. Simulation results validate the performance of the proposed adaptive observer.

Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

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.

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.

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.

In this paper, we introduce a vector which is able to describe the Niederlinski Index (NI), Relative Gain array (RGA), and the characteristic equation of the relative error matrix. The spectral radius and the structured singular value of the relative error matrix are investigated. The cases where the perfect result of the Relative Gain Array, equal to the identity matrix, coincides with the least interaction in a plant are pointed out. Then, the Jury Algorithm is adopted to get some insight into interaction analysis of multivariable plants. In particular, for interaction analysis of 3×3 plants, simple yet promising conditions in terms of the Relative Gain Array and the NiederLinski Index are derived. Several examples are also discussed to illustrate the main points.

In this paper the use of proportional-integralderivative (PID) switching controllers is proposed for the control of a magnetically actuated mass-spring-damper system which is composed of two masses M1 and M2; each mass is jointed to its own spring. Two different modes occur during the system motion; a PID controller is designed for each mode and a switching logic is applied in order to recognize the system's position to switch to the proper controller. Finally, simulation results are employed to show the performance of the proposed switched PID controller. Also, comparison results with the previously used model predictive controller (MPC) are provided.

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.

Recently a lot of works have been done to detect faults in nonlinear systems. In this paper a new method, based on parity relations for linear systems, is proposed to detect faults in nonlinear systems that can be modeled by Takagi-Sugeno (TS) fuzzy system. This method is an intuitive generalization of parity relations, because TS fuzzy system uses local linear models. Results of simulation and implementation on a rotary inverted pendulum show that faults can be detected very well.

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.

This paper presents a new hybrid control strategy for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. We employed a combination of LQR controller and fuzzy-neural network in a feedback error learning framework. In the proposed control approach, LQR controller as a classical controller is designed such that the stability is guaranteed and the control purposes are satisfied. Then an intelligent controller (FNN) which is working with the classical controller (LQR) takes the control task completely. It is shown that this technique (fuzzy-LQR) has good performance and also it has a very fast and proper response. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.

In this paper, a robust water level control system for the horizontal steam generator (SG) using the quantitative feedback theory (QFT) method is presented. To design a robust QFT controller for the nonlinear uncertain SG, control oriented linear models are identified. Then, the nonlinear system is modeled as an uncertain linear time invariant (LTI) system. The robust designed controller is applied to the nonlinear plant model. This nonlinear model is based on a locally linear neuro-fuzzy (LLNF) model. This model is trained using the locally linear model tree (LOLIMOT) algorithm. Finally, simulation results are employed to show the effectiveness of the designed QFT level controller. It is shown that it will ensure the entire designer’s water level closed loop specifications.

In this 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.

In this paper, a new algorithm is presented in using Multi Layer Perceptron (MLP) and Radial Base Function (RBF) to predict Ischemia diseases by Electrocardiogram (ECG) signals. The process would be very difficult due to non-stationary and nonlinear characteristics of ECG signals. MLP and RBF algorithms are well known in predicting the problems. However, they have not been used for real time prediction through signals, especially bio signals such as ECG. Pre-processing is necessary for ECG signal in order to detect QRS complex. Regarding the extract influential features in Ischemia disease, the baseline wandering and noise suppression are done. MLP and RBF, the predictors, are employed to foresee the further next beats in ECG signals. The validity of predictor accuracy is evaluated by Root Mean Square Error (RMSE) criterion. After the prediction stage, The predicted beats are classified by Adaptive Neuro-Fuzzy network (ANFIS) classifier as ischemic and normal. MLP and RBF are tested for their abilities in order to predict Ischemic Heart Disease (IHD) upon ECG signals. The performances of classified beats are evaluated based on computed Sensitivity (Se) and Specificity (Sp). In this study several ECG signals recorded by European Society of Cardiology for ST-T database are used. By applying prediction methods (Direct and Recursive Predictions) 48 steps can be predicted ahead in ECG signal. Then the predicted beats are classified as Ischemic or normal beats. Therefore, the ischemic beats can be predicted in 48 steps ahead. By comparing the results obtained in this study, the MLP and RBF networks are evaluated for their capabilities in predicting Ischemia. According to this comparison, MLP shows better results and the results of ANFIS as a classifier has been satisfactory enough in classification of Ischemic beats. Therefore, these results can be used for early diagnosis of Ischemic Heart Disease (IHD).

A 3-DOF image stabilizer (periscope) is modeled and controlled, such that target is fixed in the center of camera image. Its nonlinear dynamic equations are extracted by applying the Euler-Lagrange equation of rigid body motion in presence of friction. The extracted equations are second order motion equations. Moreover, fuzzy sliding mode controller (FSMC) for target tracking is applied to this nonlinear system where it is subject to uncertainty and external disturbances. The controller is MIMO and it has knowledge-based structure. This method eliminates chattering that exists in the conventional sliding mode. The effectiveness of the developed algorithm is validated by simulation results.

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.

In this paper, a Genetic-AIS (Artificial Immune System) algorithm is introduced for PID (Proportional-Integral-Derivative) controller tuning using a multi-objective optimization framework. This hybrid Genetic-AIS technique is faster and accurate compared to each individual Genetic or AIS approach. The auto-tuned PID algorithm is then fused in an Immune feedback law based on a nonlinear proportional gain to realize a new PID controller. Immune algorithm presents a promising scheme due to its interesting features such as diversity, distributed computation, adaptation and self monitoring. Accordingly, this leads to a more effective Immune-based tuning than the conventional PID tuning schemes benefiting a multi-objective optimization prospective. Integration of Genetic-AIS algorithm with Immune feedback mechanism results into a robust PID controller which is ultimately evaluated via simulation control test scenarios to demonstrate quick response, good robustness, and satisfactory overshoot and disturbance rejection characteristics.

In this paper control of the xy pedestal axes has been studied which is a two degrees of freedom earth station antenna of pedestal HDF family group (High Dynamic Full Motion Leo Satellite Tracking Pedestals). This system model is achieved by using the Dymola software that according to the comparisons which have been done is very close to the actual system model and has very high accuracy. Purpose: is to track a LEO orbit satellite that of passing satellites and angles related to the antenna have been extracted path from KNTUSAT software. In carried out simulation, the operation of PI controller has been optimized, MPC and GPC has been studied. The results of comparison between simulation methods show that predictive controller has had fewer errors in satellite tracking and has been shown less control effort and also has had good behavior in disturbance rejection.

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.

In this paper, the stabilization of linear time-invariant systems with fractional derivatives using a limited number of available state feedback gains, none of which is individually capable of system stabilization, is studied. In order to solve this problem in fractional order systems, the linear matrix inequality (LMI) approach has been used for fractional order systems. A shadow integer order system for each fractional order system is defined, which has a behavior similar to the fractional order system only from the stabilization point of view. This facilitates the use of Lyapunov function and convex analysis in systems with fractional order 1

In this paper, a new approach for input-output pairing for stable and linear time invariant multivariable systems based on inputs-outputs correlation is introduced. Being independent from system's model is the characteristic of the proposed method. It is demonstrated that both static and dynamic properties of the system regarded in the proposed method. Through examples, the accuracy of the proposed approach is investigated. Finally, an example is used to show that in some cases Effective Relative Gain Array (ERGA) leads to improper pairs while the proposed method achieves the appropriate pairs.

In this study, a new type of training the adaptive network-based fuzzy inference system (ANFIS) is presented by applying different types of the Differential Evolution branches. The TSK-type consequent part is a linear model of exogenous inputs. The consequent part parameters are learned by a gradient descent algorithm. The antecedent fuzzy sets are learned by basic differential evolution (DE/rand/1/bin) and then with some modifications in it. This method is applied to identification of the nonlinear dynamic system, prediction of the chaotic signal under both noise-free and noisy conditions and simulation of the two-dimensional function. Instead of DE/rand/1/bin, this paper suggests the complex type (DE/current-to-best/1+1/bin & DE/rand/1/bin) on predicting of Mackey-glass time series and identification of a nonlinear dynamic system revealing the efficiency of proposed structure. Finally, the method is compared with pure ANFIS to show the efficiency of this method.

This paper presents a variable structure control and anti control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. Based on Lyapunov stability theory for variable structure control, the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti-control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction, and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSER's flexible-joint manipulator.

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.

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.

Time delays are common in industrial processes. The information about the delay value of any process is valuable for both identification and control procedures. Several methods have been suggested for time delay estimation (TDE) in the literature. We propose a simple method based on plant input-output data. The concept of this data driven method is from combination of two well known approaches: Time delay estimation from impulse response and subspace identification. This method can be easily implemented in Multi Input-Multi Output (MIMO) plants. Also, by analyzing the window of input-output data in an online fashion, we can utilize our proposed method for time varying delay case. To verify the effectiveness of our proposed method, the developed procedures are applied to a pH plant model, a MIMO system and a time delay varying scenario. Simulation results demonstrate the effectiveness of the proposed method.

In this study, adaptive control of flexible link model which is non-minimum phase and single- input, multiple-output (SIMO) is presented. The controllers designed aim to control the hub position in a way that attenuates the tip deflections with less energy consumption. Methods used to design the under actuated controller are WRBF network and neuro-fuzzy network and are compared to LQR and non-adaptive fuzzy controller. Learning method performed for adaptive schemes is emotional. Simulation results show the effectiveness of the designed controllers and reduction of energy demand in intelligent adaptive controllers.

This paper presents a new tuning strategy for Generalized Predictive Controllers (GPC) based on Analysis of Variance (ANOVA). This strategy is derived for Second Order plus Dead Time (SOPDT) models of an industrial plant. Moreover, SOPDT modeling allows oscillating modes to be included in the model dynamics. The tuning strategy employs a simple expression for the tuning parameter as a function of plant parameters which is absent in earlier tuning attempts. This novel expression is extracted using ANOVA method combined with nonlinear regression. Also, a better performance index value and more convenient implementation are obtained in comparison with the conventional GPC tuning methods. Therefore, the tuning strategy for SOPDT models is both more comprehensive and more effective than traditional First Order plus Dead Time (FOPDT) model tunings. The proposed strategy is verified by two comparative simulation studies.

This paper presents a new approach for breast cancer detection based on Hierarchical Fuzzy Neural Network (HFNN). Generally in formal fuzzy neural networks (FNN), increasing the number of inputs, causes exponential growth in the number of parameters of the FNN system. This phenomenon named as" curse of dimensionality". An approach to deal with this problem is to use the hierarchical fuzzy neural network. A HFNN consists of hierarchically connected low-dimensional fuzzy neural networks. HFNN can use less rules to model nonlinear system. This method is applied to the Wisconsin Breast Cancer Database (WBCD) to classify breast cancer into two groups: benign and malignant lesions. The performance of HFNN is then compared with FNN by using the same breast cancer dataset.

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.

In this paper, the problem of supervisory based switching Quantitative Feedback Theory (QFT) control is proposed for the control of highly uncertain plants. According to this strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a QFT controller exits for robust stability and performance of the individual uncertain sets. The proposed control architecture is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. It is shown that this strategy leads to improved closed loop performance, and can also handle the uncertainty sets that can not be tackled by a single QFT robust controller. Simulation results are proposed to show the effectiveness of the proposed methodology.

In this paper, a new feature selection and classification methods based on artificial neural network are applied to classify breast cancer on dynamic Magnetic Resonance Imaging (MRI). The database including benign and malignant lesions is specified to select the features and classify with proposed methods. It is collected from 2004 to 2006.

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.

In this paper, the relation between Input-output pairing and minimum variance (MV) index as a performance index is studied. Control structure selection or the input-output pairing problem is a key step in designing decentralized controllers for multivariable. The Relative Gain Array (RGA) is an important tool for the control structure selection procedure. In this study, RGA is calculated and decentralized minimum variance controllers are designed for each feasible pairing. The MV performance index will be calculated from the closed loop transfer function using the markov parameters. It is shown that the value of the MV index can propose an input-output pairing that leads to minimum output variance. Several simulation results are provided to show the main points of the paper.

According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy mean and root mean square (RMS), energy entropy mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy mean and root mean square is proposed. The analysis results from energy entropy and root mean square of different vibration signals show that the energy and root mean square of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run then, calculate the energy entropy mean and root mean square which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and FFT to extract the energy and root mean square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.

Multi-objective design problem is the optimization of various and often conflicting objectives for a complex system. In this paper, optimization is performed using Linear Matrix Inequalities (LMI's). A switching strategy is proposed in order to improve the multi-objective control performance. Each controller design is based on a set of performance specifications. Instead of considering all the specifications defined by respective LMI sets simultaneously, only relevant objectives are included in the control design procedure. Then, switching is performed to meet multiple objectives. Assurance of the overall stability of the closed-loop is acknowledged via specific controller realization. Multi-objective designs are prone to conservatism, which is greatly reduced by the switching approach. The efficiency of the proposed methodology is illustrated through an example.

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.

In this research, a semi active control system with continuous variations along with hydro active dampers and springs is developed for a passenger car. The improvement of dynamic behavior of a passenger car with regard to weight constraint, energy consumption and cost highlights the need for the employment of such a semi active suspension system. Here, a full car model with hydro active subsystems including roll, pitch, bounce movements, and one degree of freedom for the driver is extracted, unlike the previous research in which merely the bouncing motion has been taken into account. By using the linearized car model equipped with the proposed hydro active system, the optimal damping force based on full state feedback control and LQR method is obtained for the improvement of the ride comfort and stability. In addition, practical constraints on manufacturing of the components and delay in the control system are dealt with. The car is excited by a disturbance such as a bump imparted to the wheels for which the idea of the wheel based filtering was also considered. The simulation results in the time domain for the hydro active suspension system demonstrate significant improvements in all controlled modes in comparison with the passive system. On the other hand, in comparison with the fully active system, the proposed system has an additional advantage in terms of energy consumption and weight reduction in the required hardware.

One of the most common cardiovascular diseases is Myocardial Ischemia (MI). The aim of this study is improving the diagnosis level of Ischemic Beat detection from ECG signals which is important in ischemic episode detection process. This improvement is based on appropriate feature extraction via Multi resolution Wavelet analysis and proper classifier selection. The approach starts with signal preprocessing, Afterwards efficacious morphologic features which are useful in ischemia detection are extracted by wavelet analysis. In the third stage subtractive clustering is performed for clustering. Finally probabilistic neural networks are used as a classifier. The proposed algorithm is evaluated on the European Society of Cardiology (ESC) ST-T database and reported 96.67% sensitivity and 89.18% specificity.

To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of traffic systems. METANET model is one of the most applicable models in traffic modeling which parameters have plenty of effects on model behavior. In this paper, we describe the effects of the model parameters on the model behavior and the estimation quality of system states in the case of undetermined parameters. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic traffic networks for preparing proper signal in traffic control.

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.

In this paper we present a systematic procedure to design robust fuzzy controller for exponentially stabilizing affine nonlinear systems, based on their TS fuzzy model. For robust design we consider modeling error in TS model and as well as perturbation in the original nonlinear system. Minimization of cost function along with mapping closed loop poles to desired poles are considered simultaneously in controller design. As a result, the desired specified performance in transient response can be achieved. Piecewise Discontinues Lyapunov Functions (PDLF) are utilized in our proposed method. To avoid difficulties in boundary conditions in PDLF we opt to design an online controller and check the regions and boundaries continuously. The constraints required to guarantee the exponential stability of the original nonlinear systems and optimal controller design with guaranteeing desired performance are presented in the LMI form. The y well developed. The power of these methods is that searching Lyapunov function and feedback gain can be stated as a convex optimization problem and the task of finding the common Lyapunov function can be readily be formulated into an LMI problem. However this approach is too conservative and there are lots of stable systems that we can not find a common positive definite Lyapunov function for all subsystems. Piecewise quadratic Lyapunov function approach [7],[8] have been considered to avoid conservativeness of quadratic Lyapunov function approaches [4]-[6]. Piecewise quadratic Lyapunov function (PLF) are divided in two categories, one is continuous (PCLF) in boundaries and one of them is discontinuous (PDLF) on boundaries. It was shown that PDLF in contrast with PCLF results in fewer LMIs [9]. To apply all mentioned methods, the system must be presented by a Takagi-Sugeno model and as it was demonstrated TS modeling enables us to deal with high order complicated nonlinear systems. Most of works so far have used PCLF for controller design and stability analysis, but PDLF have been used mainly for stability analysis and there are no reports about using PDLF for controller design. The main reason is difficulties in boundary conditions. effectiveness and applicability of the proposed method is examined on an inverted pendulum system.

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.

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.

This paper investigates chaos control for scalar delayed chaotic systems using sliding mode control strategy. Sliding surface design is based on delayed feedback controller. It is shown that the proposed controller can achieve stability for an arbitrary unstable fixed point (UPF) or unstable periodic orbit (UPO) with arbitrary period. The chaotic system used in this study to illustrate the theoretical concepts is the well known Mackey–Glass model. Simulation results show the effectiveness of the designed nonlinear sliding mode controller.

This paper proposes a hybrid control scheme for the synchronization of two chaotic nonlinear gyros, subject to uncertainties and external disturbances. In this scheme, Linear Quadratic Regulation (LQR) control, Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control are combined. By Lyapunov stability theory, SM control is presented to ensure the stability of the controlled system. GRBFNN control is trained during the control process. The learning algorithm of the GRBFNN is based on the minimization of a cost function which considers the sliding surface and control effort. Simulation results demonstrate the ability of the hybrid control scheme to synchronize the chaotic gyro systems through the application of a single control signal.

Rotary cement kiln is the main part of a cement plant that clinker is produced in it. Clinker is the main ingredient of cement. Continual and prolonged operation of rotary cement kiln is vital in cement factories. However, prolonged operation of the kiln is not possible and periodic repairs of the refractory lining would become necessary, due to non-linear phenomena existing in the kiln, such as sudden falls of coatings in the burning zone and probability of damages to the refractory materials during production. This is the basic reason behind the needs for a comprehensive model which is severely necessary for better control of this process. Such a model can be derived based on the mathematic analysis with consultation of expert operator experiences. In this paper both linear and nonlinear model are identified for rotary kiln of Saveh white cement factory. The linear model is introduced using Box-Jenkins structure. The results of the obtained model were satisfactory compared to some other linear models and can be used for designing adaptive or robust controllers. Also, nonlinear system identification via Neural Network technique is performed and its result was compared to linear models.

In this paper, design of decentralized switching control for uncertain multivariable plants based on the Quantitative Feedback Theory (QFT) is considered. In the proposed strategy, the uncertainty region is divided into smaller regions with a nominal model. It is assumed that a MIMO-QFT controller exists for robust stability and performance of the individual uncertain sets. The proposed control structure is made up by these local controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the local models behaviors with the one of the plant and selects the controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching to guarantee the overall closed loop stability. It is shown that this strategy provides a stable and robust adaptive controller to deal with complex multivariable plants with input-output pairing changes during the plant operation, which can facilitate the development of a reconfigurable decentralized control. Simulation results are employed to show the effectiveness of the proposed method.

Control system design for a complex system encompasses the optimization of different and often conflicting objectives leading to a multiple objective design problem. In this paper, a switching strategy is proposed to solve the multiple objective controller design. Each controller is designed based on a set of performance specifications. Control realization considerations are used to ensure overall closed loop stability. Linear matrix inequalities are employed in the controller design process. Multi objective designs are always prone to over conservatism, which is greatly reduced by the switching strategy. Simulation results are used to show the effectiveness of the design methodology.

Brain Computer Interface one of hopeful interface technologies between humans and machines. Electroencephalogram-based Brain Computer Interfaces have become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Detecting artifacts produced in electroencephalography data by muscle activity, eye blinks and electrical noise is a common and important problem in electroencephalography research. In this research, we used five different methods for detecting trials containing artifacts. Finally we used two different neural networks, and support vector machine to classify features that are extracted by wavelet transform.

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 GRBF_NN identification, adaptive backstepping control techniques.

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.

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear identification technique on the Locally Linear Neuro-Fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise fifteen minute prediction horizon for a cement rotary kiln is presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these models. The data collected from White Saveh Cement Company is used for modeling.

This paper 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.

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.

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.

This paper suggests a novel approach for control of a flexible-link based on the feedback- error-learning (FEL) strategy. A radial basis function neural network (RBFNN) is used as an adaptive controller to improve the performance of a lead compensator controller in FEL structure. This scheme is developed by using a modified version of the FEL approach to learn the inverse dynamic of the flexible manipulator which requires only a linear model of the system for designing lead compensators and RBFNN controllers. The final controller should allow the user to command a desired tip angle position. The controller should eliminate the link's vibrations while maintaining a desirable level of response. Finally, the control performance of the proposed control approach for tip position tracking of flexible-link manipulator is illustrated by simulation result.

Objectives: The aim of this study was to evaluate the outcome of a group of patients with bipolar I disorder admitted to Roozbeh Hospital, Tehran, Iran, during one year follow up. Method: In this prospective naturalistic study, 131 subjects with bipolar I disorder who were consecutively admitted to the hospital were enrolled. Patients were assessed at baseline, discharge, and 6 and 12 months after admission to the hospital. Different aspects of response to treatment including severity of mood and psychotic symptoms, extrapyramidal side effects, global functioning and service satisfaction were assessed using a demographic questionnaire, Young Mania Rating Scale (Y-MRS), and Hamilton Rating Scale for Depression (HAM-D). Results: Severity of symptoms and function showed significant improvement only at discharge (p<0.001), and was not significant afterwards. Patients showed a response rate of 65.4% based on 50% decrease on (Y-MRS), at discharge. Conclusion: Improvement in symptom severity and global functioning was significant at discharge but there was no significant improvement after discharge and during one year follow up.

Bioprocesses 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.

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.

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.

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.

Thyroid gland produces thyroid hormones to help the regulation of the body's metabolism. The abnormalities of producing thyroid hormones are divided into two categories. Hypothyroidism which is related to production of insufficient thyroid hormone and hyperthyroidism related to production of excessive thyroid hormone. Separating these two diseases is very important for thyroid diagnosis. Therefore support vector machines and probabilistic neural network are proposed to classification. These methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper feature selection is argued as an important problem via diagnosis and demonstrate that GAs provide a simple, general and powerful framework for selecting good subsets of features leading to improved diagnosis rates. Thyroid disease datasets are taken from UCI machine learning dataset.

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In this paper a Mamdani type fuzzy system and an adaptive network based fuzzy inference system (ANFIS) are presented for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. The architecture and learning procedure ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. It is shown that both these controllers can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of nonlinear mathematical model of the system.

In this paper, a multiple models, switching, and tuning control algorithm based on poleplacement control is studied. Drawbacks of the algorithm in disturbance rejection are discussed, and a novel supervisor to enhance the decision-making procedure is developed. The modified algorithm is evaluated in a simulation study for a nonlinear pH neutralization process. Comparison results are provided to evaluate the performance and robustness characteristics of the proposed algorithm.

In this paper, 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.

This paper introduces a new scheme for building a clean room with no unwanted sound in it. Resent efforts for building such rooms always encounter problems because of the stochastic nature of these systems. A real clean room not only faces some non-moving sources, but also faces some moving ones, that probably the sound source is a moving one in reality. In order to solve the problem of motion of those moving sources, one must design a method to solve this problem which we definitely encounter with. Active noise cancellation needs the direction of transmission of the unwanted sound in order to reduce its effect. Hence, these rooms need an identifier, which we suggest a clean room identification algorithm in this paper. Recent efforts for building these kind of room, always suppose the non-moving sound sources which we may encounter a moving one in every day life. The other problem is the mutual effect of each source on the other microphones. Those non-moving sources just cause mutual effect on all microphones and compel us using a separator. Therefore, these systems need a separator, which we use BSS algorithms in order to reach this aim. Finally, simulation results are provided to illustrate the main points.

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.

Inherent nonlinearity of pH processes causes that they are recognized as an appropriate test bench for evaluation of advanced controllers. Because of special characteristics of them, it is evident that adaptive controllers outperform others. This paper presents a comparison between a conventional adaptive controller and a switching multiple-model adaptive one in both regulation and disturbance rejection points of view. A disturbance rejection supervisor is designed to improve the performance of the adaptive controllers in the presence of unmeasured disturbances. A laboratory scale pH process is used as an application example.

Control based on multiple models (MM) is an effective strategy to cope with structural and parametric uncertainty of systems with highly nonlinear dynamics. It relies on a set of local models describing different operating modes of the system. Therefore, the performance is strongly depends on the distribution of the models in the defined operating space. In this paper, the problem of on-line construction of local model set is considered. The necessary specifications of an autonomous learning method are stated, and a high-level supervisor is designed to add an appropriate model to the available model set. The proposed algorithm is evaluated in a simulated pH neutralization process which is a highly nonlinear plant and composed of both abrupt and large continuous changes. The preference of the multiple-model approach with learning ability on a conventional 

Current state-of-the-art approaches for control of hybrid systems face two main important challenging problems which are stabilization and computational complexity. This paper aims at improving a special strategy i.e. predictive ontrol for a special class of hybrid systems i.e. mixed logical dynamical systems. For mixed logical dynamical ystems as a main class of hybrid systems, the only existing way to ensure the closed loop stability of predictive controllers is to use a terminal state equality constraint in the successive optimization problems. Limitations caused by this type of constraint have been discussed. Contractive predictive control is proposed as a good alternative which assures the closed loop stability in a more feasible manner. As a Lyapunov function, the L1 norm of the state vector is enforced to shrink in successive optimization steps. A suboptimal version of contractive MPC scheme has been proposed which reduces the computational complexity of the control problem while preserving the stability

In this paper a novel approach is proposed to solve a decentralized control problem to stabilize a multivariable system and attenuate the interconnections between its subsystems. To satisfy these conditions, an ut feedback controller is designed by solving an H∞ control problem. The designed controller is applied to a practical multivariable Flow-Level plant to show the effectiveness of the proposed methodology. The time delays, transmission zero, two different time constants, and the model uncertainties are the main problems in this plant.

Abstract A method of using particle swarm optimization (PSO) algorithm to design electromagnetic absorber is presented. To demonstrate effectiveness of the PSO algorithm three different design cases are optimized. To reduce the local minimum traps, a modified local search strategy is employed. Each design problem is optimized using genetic algorithm (GA) and four variants of PSO algorithms, namely global PSO (gbest), local PSO (lbest), comprehensive learning PSO (CLPSO), and modified local PSO (MLPSO). The results clearly show that the MLPSO is a robust, fast, and useful optimization tool for designing absorbers. A seven-layer absorber achieved by this method has reflection coefficient below 18.7 dB from VHF to 20 GHz.

In this method, firstly, the frequency responses of system in a few points are predicted and are compared with the frequency response of the model reference that is the proper loop gain function. In the next step, a second order Controller for compensating is designed. Finally, a benchmark for the convergence of the real loop to the reference function and the stability of the closed loop is introduced. As the convergence of the response is adequate in a limited band the structural information of the system such as order of the system, order and number of delays is not necessary. The application of this method is in control of high order system and the systems with delayed response.

In This paper, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL controller consists of neural network feedforward controller (NNFC) and conventional feedback controller (CFC), where the CFC is essential to guarantee global asymptotic stability of the overall system. Also, for improved the performance of system the dynamic neural network (DNN) is adopted in NNFC instead of conventional neural network. This neural network has dynamic in its structure and consists of two units: inhibitory and excitatory unit. The proposed FEL controller has been compared with the conventional FEL (CFEL) controller and the PID controller through some performance indices.

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

In this study, a new group method of data handling (GMDH) method, based on adaptive neurofuzzy inference system (ANFIS) structure, called ANFIS-GMDH and its application for diabetes mellitus forecasting is presented. Conventional neurofuzzy GMDH (NF-GMDH) uses radial basis network (RBF) as the partial descriptions. In this study the RBF partial descriptions are replaced with two input ANFIS structures and backpropagation algorithm is chosen for learning this network structure. The Prima Indians diabetes data set is used as training and testing sets which consist of 768 data whereby 268 of them are diagnosed with diabetes. The result of this study will provide solutions to the medical staff in determining whether someone is the diabetes sufferer or not which is much easier rather than currently doing a blood test. The results show that the proposed method performs better than the other models such as multi layer perceptron (MLP), RBF and ANFIS structure.

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.

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.

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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.

Neural network Based controller is used for controlling a magnetic levitation system. Feedback error learning (FEL) can be regarded as a hybrid control to guarantee stability of control approach. This paper presents simulation of a magnetic levitation system controlled by a FEL neural network and PID controllers. The simulation results demonstrate that this method is more feasible and effective for magnetic levitation system control.

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.

In this paper a Neural Predictive Controller (NPC) designed to control a broad class of process systems. Neural network identification yields nonlinear global model of the unknown system. LevenbergMarquardt (L-M) optimization method is used to find optimal control signal to minimize future errors of the objective function of predictive controller. Inequality constraints of actuators are added to the objective function through a penalty term which increases drastically as it approaches the limitations. To use the controller for wide range of process systems, an initial phase runs before the main controller to determine parameters. This phase moves the system output to operating point and applies PID controller with APRBS reference signal. The gathered data are used to estimate parameters such as pure delay, prediction horizon, control coefficient and identification order. To validate the approaches, the controller has implemented in level, pressure and flow pilot plants and compared with conventional controller which shows faster and smoother tracking results.

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS) and a new type of particle swarm optimizers (PSO). The previous works emphasized on gradient base method or least square (LS) based method. This study applied one of the swarm intelligent branches, PSO. The hybrid method composes Fuzzy PSO with recursive least square (RLS) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from Fuzzy Systems method and using Fuzzy rules for tuning PSO parameters during training algorithms. The simulation results show that in comparison with current gradient based training, and authors previous hybrid method the proposed training have a good adaptation to complex plants and train less parameter than gradient base methods.

In this study integer genetic algorithm is applied for path planning of mobile robot in the grid form environment. The novel representation is proposed for definition of chromosome which reduced the computational complexity of genetic algorithm that was used before for path planning. Comparison with other encoding of chromosome is done to show the capability of proposed algorithm. Mamadani fuzzy rule is used to describe difficulty of passing from cells which are sandy or have slope.

Performance assessment and monitoring of control systems can be used to improve the performance of industrial processes. In this paper, a novel relay feedback based method for monitoring and automatic retuning of a class of proportional-integral (PI) controllers is proposed for the systems with gain nonlinearity. For performance assessment of the closed loop system, a time domain evaluation criteria based on the integral of the absolute value of the error (IAE) and the normalized pick of the error in setpoint (SP) changes are presented. Simulation results on the highly nonlinear pH process have shown the effectiveness and feasibility of this method.

This paper, describes a hybrid control method to control a flexible joint. Dynamic equation of the system has been derived. The designed controllers consist of two parts: classical controller, which is a Linear Quadratic Regulation (LQR), and a hybrid controller, utilizing sliding mode control using Gaussian Radial Basis Function Neural Networks (RBFNN). The RBFNN is trained during the control process and it is not necessary to be trained off-line.

This study suggests new learning laws for Adaptive Network based Fuzzy Inference System that is structured on the basis of TSK type III as a system identifier. Stable learning algorithms for consequence parts of TSK type III rules are proposed on the basis of the Lyapunov stability theory and some constraints are obtained. Simulation results are given to validate the results. It is shown that instability will not occur for learning rates in the presence of constraints. The learning rate can be calculated online from the input–output data, and an adaptive learning for the Adaptive Network based Fuzzy Inference System structure can be provided.

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.

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.

Rotary Kiln is the central and the most complex components of cement production process. The first point at the beginning of the process, which is called back-end, is the calcining zone of the kiln and has a significant role on the quality of the clinker. In this paper to control the back-end temperature of a rotary kiln, we propose a fuzzy controller based on the operator's behaviors. We concentrate on how we can control the back-end temperature of a rotary kiln using a fuzzy controller. The work presented in this paper, is to use the advantages of fuzzy logic techniques to propose and develop a supervisory control system for the cement plant process having complex dynamics. The performance of the proposed controller is investigated by applying it to a simulator of the plant, derived from an MLP neural network model. Finally, the controller's behavior is evaluated in a disturbance environment. Simulation results show that the functionality of the controller causes a smooth behavior in both controller and model outputs.

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.

A comprehensive step-by-step approach to the construction of a global fuzzy TSK model for a real SISO nonlinear system in closed loop is proposed. First the overall existing nonlinear distortions are evaluated, and then sub optimal experiments are designed so that the impacts of distortion are minimized. Such experiments can result in models which are consistent local estimations of the true underlying linear system at different operating points. Finally by suitable fuzzy combination of extracted open loop local models, a global fuzzy simulation model is constructed. Employing a quasi tailor-made parameterization, a model refinement can be carried out by trimming the membership functions through any optimization algorithms. In opposite of the conventional tailor-made parameterization algorithm, the stability of this modified algorithm is guaranteed.

In this paper, we discuss a neural network based on hebbian learning rule for finding the inverse of a matrix. First we described finding the inverse of a matrix by mentioned neural network. Finally, experimental results for square and non-square matrices are presented to show the effectiveness of the approach. Proposed method is also scalable for finding the inversion of large-scale matrices.

In this study, a hybrid learning algorithm for training the recurrent fuzzy neural network (RFNN) is introduced. This learning algorithm aims to solve main problems of the gradient descent (GD) based methods for the optimization of the RFNNs, which are instability, local minima and the problem of generalization of trained network to the test data. PSO as a global optimizer is used to optimize the parameters of the membership functions and the GD algorithm is used to optimize the consequent part's parameters of RFNN. As PSO is a derivative free optimization technique, a simpler method for the train of RFNN is achieved. Also the results are compared to GD algorithm.

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.

Use of multi-objective particle swarm optimization for designing of planar multilayered electromagnetic absorbers and finding optimal Pareto front is described. The achieved Pareto presents optimal possible trade offs between thickness and reflection coefficient of absorbers. Particle swarm optimization method in comparison with most of optimization algorithms such as genetic algorithms is simple and fast. But the basic form of multi-objective particle swarm optimization may not obtain the best Pareto. We applied some modifications to make it more efficient in finding optimal Pareto front. Comparison with reported results in previous articles confirms the ability of this algorithm in finding better solutions.

In this paper, two methods of designing controller for a practical MIMO Flow-Level pilot plant are achieved. Since the open loop plant is unstable, it is stabilized by using inner controller. Then the stabilized plant is identified at different operating points. A nominal model which is non-minimum phase is selected. This system has two outputs which have completely different time constants. Therefore, the identification problem is difficult. The other problem associated with the system is the time delay that is one of the characteristics of the process plants. The transfer function of system is close to triangular and the RGA matrix of plant is close to the identity matrix. Hence, decentralized control can be a good selection for this system which is used in our work. The robustness of the system with decentralized controller is also checked. Two Hinfin, robust controllers based on the knowledge of the disturbance are derived and the results are compared to the results of the decentralized one. To have a good robust performance the mu-synthesis and DK-iteration approach is used.

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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.

Abstract Simulation of bending vibration effects on a two-stage launch vehicle and design of a new adaptive algorithm to reduce the flexible behaviours are discussed in this paper. The new adaptive algorithm uses recursive least square (RLS) method and two notch filters for decoupling rigid and flexible dynamic of the launch vehicle, estimating the bending frequency and reducing vibration effects. Applying this adaptive controller to the launch vehicle control system satisfied all design requirements. As the designed adaptive controller decouples rigid and flexible dynamics for estimating the bending frequency, it is simpler and faster than the other approaches and uses less CPU-capacity. The proposed approach validated by developing 6DoF nonlinear simulation software.

In this paper, a method for designing a stable fuzzy controller for nonlinear systems with the ability of reference tracking is introduced. First, the nonlinear equations of model helicopter is presented which is modeled by Humusoft Inc. based on linearization around system operating points, a TS fuzzy model is represented. Then, based on state feedback a TS fuzzy controller is developed, which its stability is guaranteed by nonlinear matrix Inequalities (LMI). These inequalities are solved using convex optimization methods by means of Matlab and LMI toolbox. In the following, tracking problem, control signal constraints are discussed.

This study addresses new hybrid approaches for velocity control of an electro hydraulic servosystem (EHSS) in presence of flow nonlinearities and internal friction. In our new approaches, we combined classical method based-on sliding mode control and fuzzy RBF networks. The control by using adaptive networks need plant's Jacobean, but here this problem solved by sliding surface. It is demonstrated that this new technique have good ability control performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. The controllers which introduced have big range for control the system.

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with gradient decent (GD) for training. We use PSO with some changes for training procedure parameters in antecedent part. These changes are inspired from genetic algorithm (GA) method. The simulation results show that in comparison with current GD training, the novel training can have a better adaptation to complex plants. Also, the results show this new hybrid approach optimizes ANFIS parameters faster and better parameters than gradient base method

This paper presents a neuro-fuzzy based method using local linear model trees (LOLIMOT) train algorithm for nonlinear identification of a temperature control pilot plant. Such systems include highly nonlinear behavior and it is complicated to obtain an accurate physical model. Therefore, it is necessary to use such appropriate tools providing suitable models while preventing computational complexities. The identification results of pilot plant confirm the high performance of proposed method in two operational modes.

Recently, a great amount of interest has been shown in the field of modeling and control of hybrid systems. One of the efficient methods in this area utilizes the mixed logical-dynamical (MLD) systems in the modeling. In this method, the system constraints are transformed into mixed-integer inequalities by defining some logic statements. In this paper, a system containing three tanks is modeled as a nonlinear switched system using the MLD framework. Regarding this three-tank modeling, an n-tank system is modeled and number of binary and continuous auxiliary variables and also number of mixed-integer inequalities are obtained in terms of n. Then, the system size and complexity due to increase in number of tanks are considered. It is concluded that as the number of tanks increases, the system size and complexity increase exponentially which hampers control of the system. Therefore, methods should be found which result in fewer variables

This paper has introduced a new method for feature subset selection to which less attention has been given. Most of the past works have emphasized feature extraction and classification using classical methods for these works. The main goal in feature extraction is presented data in lower dimension. One of the popular methods in feature extraction is principle component analysis (PCA). This method and similar methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper we introduced particle swarm optimization (PSO) as a simple, general, and powerful framework for selecting good subsets of features, leading to improved detection rates. We used PCA for feature extraction and support vector machines (SVMs) for classification. The goal is to search the PCA space using PSO to select a subset of eigenvectors encoding important information about the target concept of interest. Another object in this paper is to increase speed of convergence by using PSO to find the best feature. We have tested the framework in mind on challenging application like face detection. Our results illustrate the significant improvement in this case

Decentralized control is a well established approach to the control of multivariable plants. In this approach, control structure design and in particular input-output pairing is a vital stage in the design procedure. There are several methods such as RGA, balanced-realization, Hankel-norm based and gramian based approach to select the appropriate input/output pairs in linear multivariable plants. In this paper, a new input-output pairing method for stable multivariable plants is proposed. This new approach is based upon the cross-gramian matrix of SISO elementary subsystems built from the original MIMO plant. The main advantages of the method are simplicity and proposing an overall measure to choose the best input output pairs

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.

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.

Quantitative Feedback Theory (QFT) is one of most effective methods of robust controller design and can be considered as a suitable method for systems with parametric uncertainties. Particularly it allows us to obtain controllers less conservative than other methods like H∞ and µ-synthesis. In QFT method, we transform all the uncertainties and desired specifications to some boundaries in Nichols chart and then we have to find the nominal loop transfer function such that satisfies the boundaries and has the minimum high frequency gain. The major drawback of the QFT method is that there is no effective and useful method for finding this nominal loop transfer function. The usual approach to this problem involves loop-shaping in the Nichols chart by manipulating poles and zeros of the nominal loop transfer function. This process now aided by recently developed computer aided design tools proceeds by trial and error and its success often depends heavily on the experience of the loop-shaper. Thus for the novice and First time QFT user, there is a genuine need for an automatic loopshaping tool to generate a first-cut solution. In this paper, we approach the automatic QFT loop-shaping problem by using a procedure involving Linear Programming (LP) techniques and Genetic Algorithm (GA).

This paper discusses the design of a cascade controller for active suspension systems, to improve ride quality. In order to do this, in the main loop, a model reference adaptive controller is designed to attenuate disturbances due to rough roads. An internal loop provides the required control force for the main controller. The closed loop system has desired robust stability and performance in the presence of uncertainty due to time varying parameters and nonlinear dynamics of the actuator. The simulation results show the effectiveness of the suggested method in increasing ride comfort and safety while constrains of suspension system maneuverability is also satisfied

In this paper, generalized predictive control (GPC) algorithm is implemented to control an earth station antenna. Nonlinear term in motors caused by gearbox or other parts is modeled by a backlash block. Simulation results show the effectiveness of GPC method for robust control in the presence of backlash nonlinearity without a priori knowledge about upper and lower bounds of backlash. Also, adaptation mechanism as a self tuning predictive control is used to conquer environment changing

In this paper, generalized predictive control (GPC) algorithm is implemented to control an Earth station antenna with a non-minimum phase motor. Nonlinear term in motors caused by gearbox or other parts is modeled by a backlash block. Simulation results show the effectiveness of GPC method for robust control in the presence of backlash nonlinearity without a priori knowledge about upper and lower bounds of backlash. Also, adaptation mechanism as a self tuning predictive control is used to conquer environment changing

Current state-of-the-art approaches for control of hybrid systems face with two main important challenging problems which are guaranteeing the stability and the computational complexity. In this article a new approach has been proposed to guarantee the closed loop stability of a class of hybrid systems while reducing the complexity of control problem by introducing some level of suboptimality. It has been shown that using contraction constraint on the objective function results in asymptotically stable closed loop system. It has also been described that since only feasibility is sufficient for stability in the proposed approach, suboptimal control could be used to reduce the computational complexity.

In this paper we propose a new data fusion method based on particle filtering and fuzzy logic in order to adaptively integrate global positioning system and strapdown inertial navigation system (GPS/SDINS). This approach will reduce the dependence of the stable solution on stochastic properties of the system which is a function of vehicle dynamics and environmental conditions So the proposed scheme will enhance the estimation performance in comparison with generic particle filter specially in the case of facing modeling uncertainty. It will also give us more reliable solution when encountering satellite signal blockage as a probable problem in land navigation. The results have clearly demonstrated that the hybrid fuzzy particle filter would improve the guidance from the point of accuracy and robustness to the mentioned problems.

A new approach to adaptive control of chaos in non-linear discrete time systems with delayed state feedback is presented. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, Lyapunov exponents are used to select the controller gain. An effective adaptive strategy for on-line identification of Lyapunov exponents is proposed. Simulation results are provided to show the effectiveness of the proposed methodology.

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Mixed logical dynamical (MLD) modeling appears as an effective and realistic approach in modeling and control of hybrid systems. In this modeling approach, dynamical and logical constraints as well as control system design specifications are transformed into so-called mixed-integer inequalities. In this paper, the MLD framework is used for modeling of a multi-tank system as a switched nonlinear system. Control of fluid levels in multiple tanks is considered as a case study for predictive control of MLD systems. Translation of control problem specifications into mixed-integer inequalities shows the ability of MLD framework to deal with complex modeling and optimization tasks

Quantitative design of robust control systems proposes a transparent and practical controller design methodology for uncertain plants. In the case of large plant uncertainties, the resulted robust controller would be unnecessarily high order with large bandwidth. On the other hand, adaptive controllers can also tackle the control of unknown uncertain plants. However, the controller would be nonlinear and time varying. In this paper, a combined design methodology based on quantitative feedback theory (QFT) and externally excited adaptive system (EEAS) is proposed. This controller can handle large plant parameter uncertainties with lower bandwidth. Also, a random optimization technique is employed to optimally design the overall robust adaptive controller. Simulation results are used to show the effectiveness of the proposed design methodology.

This paper proposes a reconfigurable controller design method for multivariable systems, which is capable of dealing with order-change problems that may occur in an after-fault system. A new method is proposed to recover the nominal closed-loop performance after a fault occurrence in the system. This approach uses the eigenstructure assignment. Unlike the previously developed approaches, the new method can be implemented in the case when the fault leads to order change of the after-fault model. Also, it can be used to solve the problems in which the set of after-fault open-loop and closed-loop eigenvalues have common elements, especially when the system becomes uncontrollable or unobservable due to the fault. The method guarantees the stability of the reconfigured closed-loop system in the presence of output feedback. Finally, simulation results are provided to show the effectiveness of the proposed method for an aircraft model.

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.

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.

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.

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.

We investigate tracking filters in electro-optical target-tracking systems with bearing-only measurements and a stationary tracker. In passive tracking, for maintaining the target in the camera field of view, two tracking angles should be controlled. To extract the target position, there is at least one frame period latency resulting from time duration required for image processing. Three filtering methods, a simple Kalman filter, a novel filtering approach based on curve fitting on time series data, and an interactive multiple model filter, are studied. Since target range is neither available nor observable, in the all mentioned techniques, instead of applying filters to the target states (position and velocity in the space), each filter is directly applied to the tracking angles. The performance of each filter in this approach is evaluated by tracking angles error with two maneuvering targets.

In this paper 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.

Genetic algorithms for optimization of the multiple model and variable structure estimators are discussed in this paper. The estimation algorithm based on the multiple model and variable structure, are the best approach used in many systems, including maneuvering target tracking, noise recognition, etc. The RAMS algorithm, asserts that a multiple model algorithm consists of three steps: model set adaptation, initialization of model-based filters, and estimation. The first step, i.e., model set adaptation, is unique for VSMM algorithm and is the only superiority of the VSMM over FSMM. After the graph theory is used for this step and the sub-optimal switching digraph algorithm is discussed, we try to use the genetic algorithm for optimizing the thresholds used in the sub-optimal algorithm. The simulations show the improvement of the system performance when we use the optimal variable structure multiple model approach.

The existing methods of decentralized control suffer from two major restrictions. First, almost all of them hinge on Lyapunov's method, and second, they do not address the problem of performance robustness. A novel methodology to overcome the above defects is presented in this paper. Central to this approach is the notion of a finite-spectrum-equivalent descriptor system in the input-output decentralized form. By way of this notion, a new formulation of the interaction which introduces some degrees of freedom into the design procedure is offered. The main result, i.e. a sufficient condition for decentralized performance stabilization in a desirable performance region and maximal robustness to unstructured uncertainties in the controller and plant parameters, nevertheless, is in terms of regular systems. Based on minimal sensitivity design of isolated subsystems via eigenstructure assignment, an analytic method for the satisfaction of the aforementioned sufficient condition is also presented.

The problem of Lyapunov Exponents (LEs) estimation from chaotic data based on Jacobian approach by polynomial models is considered. The optimum embedding dimension of reconstructed attractor is interpreted as suitable order of model. Therefore, based on global polynomial mode ling of system, a novel criterion for selecting the embedding dimension is presented. By considering this dimension as the model order, the best nonlinearity degree of polynomial is estimated. The selected structure is used for local estimating of Jacobians to calculate the LEs. This suitable structure of polynomial model leads to better results and avoids of sporious LEs. Simulation results show the effectiveness of proposed methodology.

In this paper, 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.‏

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.

Because of increasingly application of fuzzy systems, great deal of attention has been paid to design of fuzzy controller system. Many methods of fuzzy controller design have been suggested such as genetic algorithm and neural network. In this paper, our purpose is to design fuzzy controller rule base by GA. In fact, by GA through possible rule base we search for a subset of optimal rules for fuzzy control of ball and plate system. Design of this controller is based on linear form of ball and plate system with the aid of Simulink and finally the result is implemented on plant.

The input-output pairing of multivariable plants with parametric uncertainty can vary in the face of large plant parameter variations. The Relative Gain Array (RGA) analysis is a powerful tool for the input-output pairing of linear multivariable plants. In the case of parametric uncertainties, RGA elements may vary accordingly. Hence, a test is proposed to identify the change in the input-output pairing in the presence of parametric uncertainties.

In this paper, 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.

Quantitative Feedback Theory (QFT) is one of the most effective methods of robust controller design. In QFT design, we can consider the phase information of the perturbed plant so it is less conservative than H∞ and µ-synthesis methods. In this paper, we want to overcome the major drawback of QFT method, i.e., lack of an automated technique for loop-shaping. Clearly such an automatic process must involve some sort of optimization, and while recent results on convex optimization have found fruitful applications in other areas of control theory we have tried to use LMI theory for automating the loop-shaping step of QFT design.

This paper 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.

Necessary and sufficient conditions for minimum sensitivity (highest robustness) to unstructured uncertainty in linear output feedback design are presented. The approach is analytical and simple, and the solution is explicit, in compact form, and restriction-free. Genetic algorithm is employed to implement the proposed method.

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.

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.

It has been previously shown that the dynamics governing the share prices in Tehran Stock Exchange can be considered as a chaotic time series. Due to the initial sensitivity of the price generating process, it is shown that linear classical models such as ARIMA and ARCH are not able to efficiently model the dynamic of share prices in Tehran stock exchange for long term prediction purposes. However, non-linear neural network models are proposed to model the Tehran price index (TEPIX) daily data process and it is shown that such nonlinear models can successfully be used for the long term prediction of TEPIX daily data. Real data for the period of 1996 to 1999 are used to validate the prediction results.

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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.

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.

A method for Multiple identification based on the self-organizing map neural networks is presented and some of its properties is investigated. Inputs to the NN are instantaneous parameters and so the reference vectors of the networks outputs are the parameter estimation of the multiple models.

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.

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.

The adaptive stabilizer can overcome the problem of parameter variations but will result in a very complex control system compared to the conventional or even state-feedback type stabilizers. It also has its own problems such as convergence or stability in the presence of unmodelled dynamics. To overcome the problem of parameter variations and to maintain the simplicity of the stabilizer for practical implementations, a robust power system stabiliser (PSS) is proposed in this paper using the quantitative feedback theory. In the present design the power system under consideration is a single synchronous generator connected to an infinite bus through a transmission line. The control ratio is modelled, the templates of the power system are formed for a wide range of frequencies, the U-Contour and different bounds for power system uncertainties are determined. After shaping the open-loop transfer function of the control system, the controller is finally designed.

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.

In order to circumvent the need for mathematical models of multivariable plants expressed in either state-space or transfer function matrix form, tunable digital PID controllers were introduced by Porter and Jones (1986). The controller matrices can be directly determined from open-loop tests performed on asymptotically stable plants. However, although such controllers are intrinsically robust, some degradation in closed-loop behaviour inevitably occurs in the case of large plant parameter variations. In order to avoid such performance degradation, adaptive digital PID controllers were therefore introduced by Jones and Porter (1987). Such controllers incorporate fast online recursive identifiers which provide updated step-response matrices for inclusion in control laws with the structure of the earlier controllers. The effectiveness of these controllers is illustrated by examples of tunable and adaptive digital set-point tracking PID controllers for a three-input/three-output dynamically complex warship.

It is shown that, by incorporating 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.