Showing papers on "Fuzzy control system published in 1998"

Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs

Abstract: This paper presents new relaxed stability conditions and LMI- (linear matrix inequality) based designs for both continuous and discrete fuzzy control systems. They are applied to design problems of fuzzy regulators and fuzzy observers. First, Takagi and Sugeno's fuzzy models and some stability results are recalled. To design fuzzy regulators and fuzzy observers, nonlinear systems are represented by Takagi-Sugeno's (TS) fuzzy models. The concept of parallel distributed compensation is employed to design fuzzy regulators and fuzzy observers from the TS fuzzy models. New stability conditions are obtained by relaxing the stability conditions derived in previous papers, LMI-based design procedures for fuzzy regulators and fuzzy observers are constructed using the parallel distributed compensation and the relaxed stability conditions. Other LMI's with respect to decay rate and constraints on control input and output are also derived and utilized in the design procedures. Design examples for nonlinear systems demonstrate the utility of the relaxed stability conditions and the LMI-based design procedures.

Learning from Data: Concepts, Theory, and Methods

Abstract: From the Publisher: This is an interdisciplinary book on neural networks, statistics and fuzzy systems. A unique feature is the establishment of a general framework for adaptive data modeling within which various methods from statistics, neural networks and fuzzy logic are presented. Chapter summaries, examples and case studies are also included.[Includes companion Web site with ... Software for use with the book.

Fuzzy Modeling for Control

Abstract: From the Publisher: Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the design of nonlinear controllers based on fuzzy models. The main features of the presented techniques are illustrated by means of simple examples. In addition, three real-world applications are described. Finally, software tools for building fuzzy models from measurements are available from the author.

Fuzzy decision trees: issues and methods

Abstract: Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. They have undergone a number of alterations to deal with language and measurement uncertainties. We present another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation. The intent is to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation. The merger utilizes existing methodologies in both areas to full advantage, but is by no means trivial. In particular, knowledge inferences must be newly defined for the fuzzy tree. We propose a number of alternatives, based on rule-based systems and fuzzy control. We also explore capabilities that the new framework provides. The resulting learning method is most suitable for stationary problems, with both numerical and symbolic features, when the goal is both high knowledge comprehensibility and gradually changing output. We describe the methodology and provide simple illustrations.

Analysis and design of fuzzy controller and fuzzy observer

Abstract: This paper addresses the analysis and design of a fuzzy controller and a fuzzy observer on the basis of the Takagi-Sugeno (T-S) fuzzy model. The main contribution of the paper is the development of the separation property; that is, the fuzzy controller and the fuzzy observer can be independently designed. A numerical simulation and an experiment on an inverted pendulum system are described to illustrate the performance of the fuzzy controller and the fuzzy observer.

Stability analysis and design of fuzzy control systems

Abstract: The stability of a fuzzy feedback control system consisting of a fuzzy controller connected in series with a plant described by a fuzzy model is discussed. The stability analysis is based on some new theorems that guarantee sufficient conditions for asymptotical stability of the equilibrium point and total stability of the system. The stability analysis results are used to provide an approach to fuzzy controller design. The steps of the approach are specified through a design example.

Conditional fuzzy clustering in the design of radial basis function neural networks

Abstract: This paper is concerned with the use of radial basis function (RBF) neural networks aimed at an approximation of nonlinear mappings from R/sup n/ to R. The study is devoted to the design of these networks, especially their layer composed of RBF, using the techniques of fuzzy clustering. Proposed is an idea of conditional clustering whose main objective is to develop clusters (receptive fields) preserving homogeneity of the clustered patterns with regard to their similarity in the input space as well as their respective values assumed in the output space. The detailed clustering algorithm is accompanied by extensive simulation studies.

Fuzzy inference system learning by reinforcement methods

Abstract: Fuzzy Actor-Critic Learning (FACL) and Fuzzy Q-Learning (FQL) are reinforcement learning methods based on dynamic programming (DP) principles. In the paper, they are used to tune online the conclusion part of fuzzy inference systems (FIS). The only information available for learning is the system feedback, which describes in terms of reward and punishment the task the fuzzy agent has to realize. At each time step, the agent receives a reinforcement signal according to the last action it has performed in the previous state. The problem involves optimizing not only the direct reinforcement, but also the total amount of reinforcements the agent can receive in the future. To illustrate the use of these two learning methods, we first applied them to a problem that involves finding a fuzzy controller to drive a boat from one bank to another, across a river with a strong nonlinear current. Then, we used the well known Cart-Pole Balancing and Mountain-Car problems to be able to compare our methods to other reinforcement learning methods and focus on important characteristic aspects of FACL and FQL. We found that the genericity of our methods allows us to learn every kind of reinforcement learning problem (continuous states, discrete/continuous actions, various type of reinforcement functions). The experimental studies also show the superiority of these methods with respect to the other related methods we can find in the literature.

A unified approach to controlling chaos via an LMI-based fuzzy control system design

Abstract: This paper presents a unified approach to controlling chaos via a fuzzy control system design based on linear matrix inequalities (LMI's). First, Takagi-Sugeno fuzzy models and some stability results are recalled. To design fuzzy controllers, chaotic systems are represented by Takagi-Sugeno fuzzy models. The concept of parallel distributed compensation is employed to determine structures of fuzzy controllers from the Takagi-Sugeno fuzzy models, LMI-based design problems are defined and employed to find feedback gains of fuzzy controllers satisfying stability, decay rate, and constraints on control input and output of fuzzy control systems. Stabilization, synchronization, and chaotic model following control for chaotic systems are realized via the unified approach based on LMIs. An exact linearization (EL) technique is presented as a main result in the stabilization. The EL technique also plays an important role in the synchronization and the chaotic model following control. Two cases are considered in the synchronization. One is the feasible case of the EL problem. The other is the infeasible case of the EL problem. Furthermore, the chaotic model following control problem, which is more difficult than the synchronization problem, is discussed using the EL technique. Simulation results show the utility of the unified design approach based on LMIs proposed in this paper.

Adaptive fuzzy sliding mode control of nonlinear system

Abstract: In this paper, the fuzzy approximator and sliding mode control (SMC) scheme are considered. We propose two methods of adaptive SMC schemes that the fuzzy logic systems (approximators) are used to approximate the unknown system functions in designing the SMC of nonlinear system. In the first method, a fuzzy logic system is utilized to approximate the unknown function f of the nonlinear system x/sup n=/f(x, t)+b(x, t)u and the robust adaptive law is proposed to reduce the approximation errors between the true nonlinear functions and fuzzy approximators. In the second method, two fuzzy logic systems are utilized to approximate the f and b, respectively, and the control law, which is robust to approximation error is also designed. The stabilities of proposed control schemes are proved and these schemes are applied to an inverted pendulum system. The comparisons between the proposed control schemes are shown in simulations.

Improving the interpretability of TSK fuzzy models by combining global learning and local learning

Abstract: The fuzzy inference system proposed by Takagi, Sugeno, and Kang, known as the TSK model in fuzzy system literature, provides a powerful tool for modeling complex nonlinear systems. Unlike conventional modeling where a single model is used to describe the global behavior of a system, TSK modeling is essentially a multimodel approach in which simple submodels (typically linear models) are combined to describe the global behavior of the system. Most existing learning algorithms for identifying the TSK model are based on minimizing the square of the residual between the overall outputs of the real system and the identified model. Although these algorithms can generate a TSK model with good global performance (i.e., the model is capable of approximating the given system with arbitrary accuracy, provided that sufficient rules are used and sufficient training data are available), they cannot guarantee the resulting model to have a good local performance. Often, the submodels in the TSK model may exhibit an erratic local behavior, which is difficult to interpret. Since one of the important motivations of using the TSK model (also other fuzzy models) is to gain insights into the model, it is important to investigate the interpretability issue of the TSK model. We propose a new learning algorithm that integrates global learning and local learning in a single algorithmic framework. This algorithm uses the idea of local weighed regression and local approximation in nonparametric statistics, but remains the component of global fitting in the existing learning algorithms. The algorithm is capable of adjusting its parameters based on the user's preference, generating models with good tradeoff in terms of global fitting and local interpretation. We illustrate the performance of the proposed algorithm using a motorcycle crash modeling example.

Adaptive stochastic resonance

Abstract: This paper shows how adaptive systems can learn to add an optimal amount of noise to some nonlinear feedback systems. This "stochastic resonance" (SR) effect occurs in a wide range of physical and biological systems. The noise energy can enhance the faint periodic signals or faint broadband signals that force the dynamical systems. Fuzzy and other adaptive systems can learn to induce SR based only on samples from the process. The paper derives the SR optimality conditions that any stochastic learning system should try to achieve. The adaptive system learns the SR effect as the system performs a stochastic gradient ascent on the signal-to-noise ratio. The stochastic learning scheme does not depend on a fuzzy system or any other adaptive system. Simulations test this SR learning scheme on the popular quartic-bistable dynamical system and on other dynamical systems. The driving noise types range from Gaussian white noise to impulsive noise to chaotic noise.

Decoupled fuzzy sliding-mode control

Abstract: A decoupled fuzzy sliding-mode controller design is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear systems with only five fuzzy control rules. The ideas behind the controller are as follows. First, decouple the whole system into two second-order systems such that each subsystem has a separate control target expressed in terms of a sliding surface. Then, information from the secondary target conditions the main target, which, in turn, generates a control action to make both subsystems move toward their sliding surface, respectively. A closely related fuzzy controller to the sliding-mode controller is also presented to show the theoretical aspect of the fuzzy approach in which the characteristics of fuzzy sets are determined analytically to ensure the stability and robustness of the fuzzy controller. Finally, the decoupled sliding-mode control (SMC) is used to control three highly nonlinear systems and confirms the validity of the proposed approach.

On fuzzy logic applications for automatic control, supervision, and fault diagnosis

Abstract: The degree of vagueness of variables, process description, and automation functions is considered and is shown. Where quantitative and qualitative knowledge is available for design and information processing within automation systems. Fuzzy-rule-based systems with several levels of rules form the basis for different automation functions. Fuzzy control can be used in many ways, for normal and for special operating conditions. Experience with the design of fuzzy controllers in the basic level is summarized, as well as criteria for efficient applications. Different fuzzy control schemes are considered, including cascade, feedforward, variable structure, self-tuning, adaptive and quality control leading to hybrid classical/fuzzy control systems. It is then shown how fuzzy logic approaches can be applied to process supervision and to fault diagnosis with approximate reasoning on observed symptoms. Based on the properties of fuzzy logic approaches the contribution gives a review and classification of the potentials of fuzzy logic in process automation.

Electric Power Applications of Fuzzy Systems

Abstract: From the Publisher: This book offers an introduction to applications of fuzzy system theory to selected areas of electric power engineering. It presents theoretical background material from a practical point of view and then explores a number of applications of fuzzy systems. Most recently, there has been a tremendous surge in research and application articles on this subject. Until now though, there have been no books that put together a practical guide to the fundamentals and applications aspects. ELECTRIC POWER APPLICATIONS OF FUZZY SYSTEMS presents, under one cover, original contributions by authors who have pioneered in the application of fuzzy system theory to the electric power engineering field. Each chapter contains both an introduction to and a state-of-the-art review of each application area.Sponsored by:IEEE Power Engineering Society.

Uncertainty reasoning based on cloud models in controllers

Abstract: The methodology of fuzzy reasoning has been shown to be very useful technology for modeling complex nonlinear systems However, the most commonly used method for reasoning with fuzzy systems models, the Mamdani-Zadeh paradigm, faces many criticisms, particularly from the probability community A new mathematical representation of linguistic concepts is presented in this paper With the new model of normal compatibility clouds and a virtual rule engine, a novel uncertainty reasoning technology is proposed It not only serves as a foundation of linguistic control, but also integrating fuzziness and randomness in an inseparable way A case study is given to clean up many doubts raised in the debate between fuzzy theory and probability theory researchers, and to give a good interpretation of the Mamdani-Zadeh operations for the defuzzification strategy as well The architecture of such a controller shows the advantages in hardware implementations

A comprehensive study of control design for an autonomous helicopter

Abstract: We compare three different control methodologies for helicopter autopilot design: linear robust multivariable control, fuzzy logic control with evolutionary tuning, and nonlinear tracking control. The control design is based on nonlinear dynamic equations with a simplified thrust-torque generation model valid for hovering and low velocity flight. We verify the controller performance in various simulated manoeuvres.

A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems

Abstract: Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GAs). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of a FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.

Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller

Abstract: Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output "small gain theorem", the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance.

Fuzzy-neuro approach to fault classification for transmission line protection

Abstract: This paper presents a new approach to real-time fault detection and classification in power transmission systems by using fuzzy-neuro techniques. The integration with neural network technology enhances fuzzy logic systems on learning capabilities. The symmetrical components in combination with three line currents are utilized to detect fault types such as single line-to ground, line-to-line, double line-to-ground and three line-to-ground, and then to define the faulty line. Computer simulation results are shown in this paper and they indicate this approach can be used as an effective tool for high speed digital relaying, as the correct detection is achieved in less than 10 ms.

Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle

Abstract: In a parallel-type hybrid electric vehicle (HEV), torque assisting and battery recharging control using the electric machine is the key point for efficient driving. In this paper, by adopting the decision-making property of fuzzy logic, the driving map for an HEV is made according to driving conditions. In this fuzzy logic controller, the induction machine torque command is generated from the acceleration pedal stroke and its rotational speed. To construct a proper rule base of fuzzy logic, the dynamo test and road tests for a hybrid powertrain are carried out, where the torque and the nitrogen oxides (NO/sub x/) emission characteristic of the diesel engine and the driver's driving patterns are acquired, respectively. An HEV, a city bus for shuttle service, with the proposed fuzzy-logic-based driving strategy was built and tested at a real service route. It reveals that the improved NO/sub x/ emission and better charge balance without an extra battery charger over the conventional deterministic-table-based strategy.

Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications

Abstract: From the Publisher: Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural networks, fuzzy systems, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another. This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

Adaptive fuzzy sliding-mode control for PM synchronous servo motor drives

Abstract: An adaptive fuzzy sliding-mode control system, which combines the merits of sliding-mode control, the fuzzy inference mechanism and the adaptive algorithm, is proposed. First a sliding-mode controller with an integral-operation switching surface is designed. Then a fuzzy sliding-mode controller is investigated in which a simple fuzzy inference mechanism is used to estimate the upper bound of uncertainties. The fuzzy inference mechanism with centre adaptation of membership functions is investigated to estimate the optimal bound of uncertainties. Position control of a permanent magnet synchronous servo motor drive using the proposed control strategies is illustrated.

Compensatory neurofuzzy systems with fast learning algorithms

Abstract: In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart-pole balancing system and nonlinear system modeling have shown that: 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF-THEN rules from either well-defined initial data or ill-defined data; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree.

Service restoration of primary distribution systems based on fuzzy evaluation of multi-criteria

Abstract: Due to many conflicting goals, the power service restoration problem is a multiple-criteria decision making problem. This paper proposes a new restoration strategy consisting of two steps: candidate set generation and fuzzy decision making, which can generate the most preferable plan. The object-oriented paradigm has been adopted in the development of the system and three models-object model, functional model, dynamic model-have been designed. The test results have shown the effectiveness of the proposed method.

On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system

Abstract: A novel approach for enhancing the results of fuzzy clustering by imposing spatial constraints for solving image segmentation problems is presented. We have developed a Sugeno (185) type rule-based system with three inputs and 11 rules that interacts with the clustering results obtained by the well-known fuzzy c-means (FCM) and/or possibilistic c-means (PCM) algorithms. It provides good image segmentations in terms of region smoothness and elimination of the effects of noise. The results of the proposed rule-based neighborhood enhancement (RB-NE) system are compared to well-known segmentation algorithms using stochastic field modeling. They are found to be of comparable quality, while being of lower computational complexity.