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Showing papers on "Fuzzy control system published in 1996"


Book
20 Aug 1996

2,938 citations


Journal ArticleDOI
TL;DR: The authors represent a nonlinear plant with a Takagi-Sugeno fuzzy model with a model-based fuzzy controller design utilizing the concept of the so-called "parallel distributed compensation" and presents a design methodology for stabilization of a class of nonlinear systems.
Abstract: Presents a design methodology for stabilization of a class of nonlinear systems. First, the authors represent a nonlinear plant with a Takagi-Sugeno fuzzy model. Then a model-based fuzzy controller design utilizing the concept of the so-called "parallel distributed compensation" is employed. The main idea of the controller design is to derive each control rule so as to compensate each rule of a fuzzy system. The design procedure is conceptually simple and natural. Moreover, the stability analysis and control design problems can be reduced to linear matrix inequality (LMI) problems. Therefore, they can be solved efficiently in practice by convex programming techniques for LMIs. The design methodology is illustrated by application to the problem of balancing and swing-up of an inverted pendulum on a cart.

2,534 citations


Book
01 May 1996
TL;DR: Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy systems.
Abstract: Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy Systems It includes Matlab software, with a Neural Network Toolkit, and a Fuzzy System Toolkit

1,545 citations


Journal ArticleDOI
TL;DR: New stability conditions for a generalized class of uncertain systems are derived from robust control techniques such as quadratic stabilization, H/sup /spl infin// control theory, and linear matrix inequalities.
Abstract: This paper presents stability analysis for a class of uncertain nonlinear systems and a method for designing robust fuzzy controllers to stabilize the uncertain nonlinear systems, First, a stability condition for Takagi and Sugeno's fuzzy model is given in terms of Lyapunov stability theory. Next, new stability conditions for a generalized class of uncertain systems are derived from robust control techniques such as quadratic stabilization, H/sup /spl infin// control theory, and linear matrix inequalities. The derived stability conditions are used to analyze the stability of Takagi and Sugeno's fuzzy control systems with uncertainty which can be regarded as a generalized class of uncertain nonlinear systems, The design method employs the so-called parallel distributed compensation, important issues for the stability analysis and design are remarked. Finally, three design examples of fuzzy controllers for stabilizing nonlinear systems and uncertain nonlinear systems are presented.

1,139 citations


Book
01 Jan 1996
TL;DR: This text is the first to combine the study of neural networks and fuzzy systems, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems.
Abstract: From the Publisher: "Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems." -- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

977 citations


Book
01 Jan 1996
TL;DR: In this paper, a collection of papers written by the founder of fuzzy set theory, Lotfi A. Zadeh, is presented, which contains virtually all the major ideas in fuzzy set theories, fuzzy logic, and fuzzy systems in their historical context.
Abstract: From the Publisher: This book consists of papers written by the founder of fuzzy set theory, Lotfi A. Zadeh. Since Zadeh is not only the founder of this field but has also been the principal contributor to its development over the last 30 years, the papers contain virtually all the major ideas in fuzzy set theory, fuzzy logic, and fuzzy systems in their historical context.

863 citations


Book
01 Aug 1996
TL;DR: Soft computing as mentioned in this paper is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost, and its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning.
Abstract: Discusses soft computing, a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind. >

714 citations


Journal ArticleDOI
TL;DR: Computer simulation results confirm that the effect of both the fuzzy approximation error and external disturbance on the tracking error can be attenuated efficiently by the proposed adaptive fuzzy control algorithm.
Abstract: A fuzzy logic controller equipped with a training (adaptive) algorithm is proposed in this work to achieve H/sup /spl infin// tracking performance for a class of uncertain (model free) nonlinear single-input single-output (SISO) systems with external disturbances. An attempt is also made to create a bridge between two important control design techniques, i.e., H/sup /spl infin// control design and fuzzy control design, so as to supply H/sup /spl infin// control design with more intelligence and fuzzy control design with better performance. The perfect matching of parameters in an adaptive fuzzy logic system is generally deemed impossible. Therefore, a desired tracking performance cannot be guaranteed in the conventional adaptive fuzzy control systems. In this study, the influence of both fuzzy logic approximation error and external disturbance on the tracking error is attenuated to a prescribed level. Both indirect and direct adaptive fuzzy controllers are employed to treat this H/sup /spl infin// tracking problem. The authors' results indicate that arbitrarily small attenuation level can be achieved via the proposed adaptive fuzzy control algorithm if a weighting factor of control variable is adequately chosen. The proposed design method is also useful for the robust tracking control design of the nonlinear systems with external disturbances and a large uncertainty or unknown variation in plant parameters and structures. Furthermore, only smooth control signals are needed via the proposed control designs. Two simulation examples are given finally to illustrate the performance of the proposed methods. Computer simulation results confirm that the effect of both the fuzzy approximation error and external disturbance on the tracking error can be attenuated efficiently by the proposed method.

713 citations


Book
01 Jan 1996
TL;DR: This book takes a hands-on, desktop-applications approach to the topic of computational intelligence, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book.
Abstract: Computational intelligence is an emerging field in computer science which combines fuzzy logic, neural networks, and genetic algorithms for a flexible yet powerful approach to scientific computing. Because computational intelligence combines three interrelated, mathematically-based tools, it has a wide variety of applications, from engineering and process control to experts systems. This book takes a hands-on, desktop-applications approach to the topic, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book. Features: * Concise introduction to the concepts of fuzzy logic, neural networks, and genetic algorithms, and how they relate to one another within the context of computational intelligence. * Computational intellignece applications, including self-organizing feature maps, fuzzy calculator, evolutionary programming, and fuzzy neural networks. * Detailed case studies from engineering (F-16 flight system), systems control (mass transit scheduling), and medicine (appendicitis diagnosis). * Windows floppy disk with both source code and executable, self-contained programs for desktop implementation of all of the book's applications.

639 citations


Journal ArticleDOI
TL;DR: It is proved that with or without such knowledge both adaptive schemes can "learn" how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of a reference input.
Abstract: Stable direct and indirect adaptive controllers are presented, which use Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal for a class of continuous-time nonlinear plants with poorly understood dynamics The indirect adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics while the direct adaptive scheme allows for the incorporation of such a priori knowledge in specifying the controller We prove that with or without such knowledge both adaptive schemes can "learn" how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of a reference input In addition, for the direct adaptive scheme a technique is presented in which linguistic knowledge of the inverse dynamics of the plant may be used to accelerate adaptation The performance of the indirect and direct adaptive schemes is demonstrated through the longitudinal control of an automobile within an automated lane

591 citations


Journal ArticleDOI
01 Oct 1996
TL;DR: A general robust rule base is proposed for fuzzy two-term control, leaving the optimum tuning to the scaling gains, which greatly reduces the difficulties of design and tuning.
Abstract: Conventional fuzzy control can be considered mainly composed of fuzzy two-term control and fuzzy three-term control. In this paper, more systematic analysis and design are given for the conventional fuzzy control. A general robust rule base is proposed for fuzzy two-term control, leaving the optimum tuning to the scaling gains, which greatly reduces the difficulties of design and tuning. The digital implementation of fuzzy control is also presented for avoiding the influence of the sampling time. Based on the results of previous fuzzy two-term controllers, a simplified fuzzy three-term controller is proposed to enhance performance. A two-level tuning strategy is also planned, which first tries to set up the relationship between fuzzy proportional/integral/derivative gain and scaling gains at the high level, and optionally tunes the control resolution at low level. Simulation of different order models show the characteristics of fuzzy control, effectiveness of the new design methodologies, and advantages of the enhanced fuzzy three-term control.

Journal ArticleDOI
TL;DR: It is revealed that this type of fuzzy controller behaves approximately like a PD controller that may yield steady-state error for the control system, and a new fuzzy controller structure is proposed which retains the characteristics similar to the conventional PID controller.

Journal ArticleDOI
TL;DR: A neuro-fuzzy system with adaptive capability to extract fuzzy If Then rules from input and output sample data through learning is described and its validity and effectiveness are demonstrated using the RBF based AFS.

Journal ArticleDOI
TL;DR: An adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems and a method for selecting significant fuzzy rules by pruning unnecessary fuzzy rules, which consists of the error correction-based learning procedure and the concept of forgetting.
Abstract: This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure, and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classification performance. That is, when a pattern is misclassified by a particular fuzzy rule, the grade of certainty of that rule is decreased. On the contrary, when a pattern is correctly classified, the grade of certainty is increased. Because the error correction-based learning procedure is not meaningful after all the given patterns are correctly classified, we cannot adjust a classification boundary in such a case. To acquire a more intuitively acceptable boundary, we propose an additional learning procedure. We also propose a method for selecting significant fuzzy rules by pruning unnecessary fuzzy rules, which consists of the error correction-based learning procedure and the concept of forgetting. We can construct a compact fuzzy rule-based classification system with high performance.

Journal ArticleDOI
TL;DR: Methods for constructing fuzzy models from process data are reviewed, and attention is paid to the choice of a suitable fuzzy model structure for the identification task.

Journal ArticleDOI
06 Oct 1996
TL;DR: In this article, the authors describe the control strategy development, design and experimental performance evaluation of a fuzzy logic based variable speed wind generation system that uses a cage type induction generator and double-sided PWM converters.
Abstract: Artificial intelligence techniques, such as fuzzy logic, neural network and genetic algorithm are showing a lot of promise in the application of power electronic systems. The paper describes the control strategy development, design and experimental performance evaluation of a fuzzy logic based variable speed wind generation system that uses a cage type induction generator and double-sided PWM converters. The system can feed a utility grid maintaining unity power factor at all conditions, or can supply to an autonomous load. The fuzzy logic based control of the system helps to optimize the efficiency and enhance the performance. A complete 3.5 kW generation system has been developed, designed and thoroughly evaluated by laboratory tests in order to validate the predicted performance improvements. The system gives excellent performance, and can easily be translated to a larger size in the field.

Journal ArticleDOI
TL;DR: A Fuzzy C-Means-based clustering method guided by an auxiliary (conditional) variable is introduced that reveals a structure within a family of patterns by considering their vicinity in a feature space along with the similarity of the values assumed by a certain conditional variable.

Journal ArticleDOI
01 Oct 1996
TL;DR: It is proved, for both adaptive fuzzy controllers, that all signals in the closed-loop systems are uniformly bounded; and the tracking errors converge to zero under mild conditions.
Abstract: An adaptive fuzzy controller is constructed from a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given-trajectory. In this paper, two adaptive fuzzy controllers are designed based on the Lyapunov synthesis approach. We require that the final closed-loop system must be globally stable in the sense that all signals involved (states, controls, parameters, etc.) must be uniformly bounded. Roughly speaking, the adaptive fuzzy controllers are designed through the following steps: first, construct an initial controller based on linguistic descriptions (in the form of fuzzy IF-THEN rules) about the unknown plant from human experts; then, develop an adaptation law to adjust the parameters of the fuzzy controller on-line. We prove, for both adaptive fuzzy controllers, that: (1) all signals in the closed-loop systems are uniformly bounded; and (2) the tracking errors converge to zero under mild conditions. We provide the specific formulas of the bounds so that controller designers can determine the bounds based on their requirements. Finally, the adaptive fuzzy controllers are used to control the inverted pendulum to track a given trajectory, and the simulation results show that: (1) the adaptive fuzzy controllers can perform successful tracking without using any linguistic information; and (2) after incorporating some linguistic fuzzy rules into the controllers, the adaptation speed becomes faster and the tracking error becomes smaller.

Journal ArticleDOI
TL;DR: The proposed fuzzy control scheme is evaluated by computer simulations as well as experimental measurements of the closed-loop performance of simple DC/DC power converters in respect of load regulation and line regulation.
Abstract: The design of a fuzzy logic controller for DC/DC power converters is described in this paper. A brief review of fuzzy logic and its application to control is first given. Then, the derivation of a fuzzy control algorithm for regulating DC/DC power converters is described in detail. The proposed fuzzy control scheme is evaluated by computer simulations as well as experimental measurements of the closed-loop performance of simple DC/DC power converters in respect of load regulation and line regulation.

Proceedings ArticleDOI
24 Jun 1996
TL;DR: The basic theory of FCNN is presented, a generalization of CNN by using fuzzy operations in the synaptic law calculation allowing us to combine the low level information processing capability of CNNs with the high level informationprocessing capability of fuzzy systems.
Abstract: Fuzzy cellular neural networks (FCNN) are novel classes of cellular neural networks. In this paper, the basic theory of FCNN is presented. FCNN is a generalization of CNN by using fuzzy operations in the synaptic law calculation allowing us to combine the low level information processing capability of CNNs with the high level information processing capability, such as image understanding, of fuzzy systems. The FCNN structures are based on the uncertainties in human cognitive processes and in modeling neural systems, and provide an interface between the human expert and the classical CNN.


Journal ArticleDOI
TL;DR: The conclusion is that the fuzzy logic approach is promising, but it suffers from the “curse of dimensionality” and can be a useful supplement to existing operating practices.
Abstract: Relatively little of the research on reservoir operating procedures has found its way into actual practice. One reason is that operators are uncomfortable with complex optimization models and reluctant to use procedures that they do not fully understand. Fuzzy logic seems to offer a way to improve on existing operating practices, which is relatively easy to explain and understand. The main concepts in fuzzy logic and a procedure for applying them are explained. These are applied to finding operating procedures for a single-purpose hydroelectric project, where both the inflows and the selling price for energy can vary. Operation of the system is simulated using both fuzzy logic programming and fixed rules. The results are compared with those obtained by deterministic dynamic programming with hindsight. The use of fuzzy logic with flow forecasts is also investigated. The conclusion is that the fuzzy logic approach is promising, but it suffers from the “curse of dimensionality.” It can be a useful supplement...


Journal ArticleDOI
01 Aug 1996
TL;DR: A hybrid neural system that combines unsupervised and supervised learning to find and tune the rules in the form of ellipsoids is used and a closed-form model for the optimal rules when only the centroids of the ellip soids change is found.
Abstract: A fuzzy rule can have the shape of an ellipsoid in the input-output state spare of a system. Then an additive fuzzy system approximates a function by covering its graph with ellipsoidal rule patches. It averages rule patches that overlap. The best fuzzy rules cover the extrema or bumps in the function. Neural or statistical clustering systems can approximate the unknown fuzzy rules from training data. Neural systems can then both tune these rules and add rules to improve the function approximation. We use a hybrid neural system that combines unsupervised and supervised learning to find and tune the rules in the form of ellipsoids. Unsupervised competitive learning finds the first-order and second-order statistics of clusters in the training data. The covariance matrix of each cluster gives an ellipsoid centered at the vector or centroid of the data cluster. The supervised neural system learns with gradient descent. It locally minimizes the mean-squared error of the fuzzy function approximation. In the hybrid system unsupervised learning initializes the gradient descent. The hybrid system tends to give a more accurate function approximation than does the lone unsupervised or supervised system. We found a closed-form model for the optimal rules when only the centroids of the ellipsoids change. We used numerical techniques to find the optimal rules in the general case.

Journal ArticleDOI
TL;DR: Fuzzy Cognitive Maps can represent the causal relationships needed for the FMEA and provide a new strategy for predicting failure effects in a complex system.

Book
01 Dec 1996
TL;DR: This chapter discusses the design and implementation of Fuzzy Control Systems' Stability Classes, and discusses the Controllability and Observability of Large-Scale Systems, which are based on the Hierarchical Control method.
Abstract: Preface. 1. Introduction to Large-Scale Systems. Historical Background. Hierarchical Structures. Decentralized Control. Artificial Intelligence. Neural Networks. Fuzzy Logic. Computer-Aided Approach. Scope. Problems. 2. Large-Scale Systems Modeling. Introduction. Aggregation Methods. General Aggregation. Modal Aggregation. Balanced Aggregation. Perturbation Methods. Weakly Coupled Models. Strongly Coupled Models. Modeling via System Identification. Problem Definition. System ID Toolbox. Modeling via Fuzzy Logic. Problems. 3. Structural Properties of Large Scale Systems. Introduction. Lyapunov Stability Methods. Definitions and Problem Statement. Stability Criteria. Connective Stability. Input-Output Stability Methods. Problem Development and Statement. IO Stability Criterion. Controllability and Observability of Composite Systems via Connectivity Approach. Preliminary Definitions. Controllability and Observability Conditions. Structural Controllability and Observability. Structure and Rank of a Matrix. Conditions for Structural Controllability. Structural Controllability and Observability via System Connectability. Computer-Aided Structural Analysis. Standard State-Space Forms. CAD Examples. Discussion and Conclusions. Discussion of the Stability of Large-Scale Systems. Discussion of the Controllability and Observability of Large-Scale Systems. Problems. 4. Hierarchical Control of Large-Scale Systems. Introduction. Coordination of Hierarchical Structures. Model Coordination Method. Goal Coordination Method. Hierarchical Control of Linear Systems. Linear System Two-level Coordination. Interaction Prediction Method. Goal Coordination and Singularities. Closed-Loop Hierarchical Control of Continuous-Time Systems. Series Expansion Approach of Hierarchical Control. Problem Formulation. Performance Index Approximation. Optimal Control. Coorinator Problem. Computer-Aided Hierarchical Control Design Examples. Problems. 5. Decentralized Control of Large-Scale Systems. Introduction. Decentralized Stabilization. Fixed Polynomials and Fixed Modes. Stabilization via Dynamic Compensation. Stabilization via Multilevel Control. Exponential Stabilization. Decentralized Adaptive Control. Decentralized Adaptation. Decentralized Regulation Systems. Decentralized Tracking Systems. Liquid-Metal Cooled Reactor. Application of Model Reference Adaptive Control. Discussion and Conclusions. Problems. 6. Near-Optimum Design of Large-Scale Systems. Introduction. Near-Optimum Control of Linear Time-Invariant Systems. Aggregation Methods. Perturbation Methods. Decentralized Control via Unconstrained Minimization. Near-Optimum Control of Large-Scale Nonlinear Systems. Near-Optimum Control via Sensitivity Methods. Hierarchical Control via Interaction Prediction. Bounds on Near-Optimum Cost Functional. Near-Optimality Due to Aggregation. Near-Optimality Due to Perturbation. Near-Optimality in Hierarchical Control. Near-Optimality in Nonlinear Systems. Computer-Aided Design. Problems. 7. Fuzzy Control Systems-Structures and Stability. Introduction. Fuzzy Control Structures. Basic Definitions and Architectures. Fuzzification. Inference Engine. Defuzzification Methods. The Inverted Pendulum Problem. Overshoot-Suppressing Fuzzy Controllers. Analysis of Fuzzy Control System. Stability of Fuzzy Control Systems. Introduction. Fuzzy Control Systems' Stability Classes. Lyapunov Stability of Fuzzy Control Systems. Fuzzy System Stability via Interval Matrix. Method. Problems. 8. Fuzzy Control Systems-Adaptation and Hierarchy. Introduction. Adaptive Fuzzy Control Systems. Adaptation by Parameter Estimation. Adaptive Fuzzy Multiterm Controllers. Indirect Adaptive Fuzzy Control. Large-Scale Fuzzy Control Systems. Hierarchical Fuzzy Control. Rule-Base Reduction. Hybrid Control Systems. Problems. Appendix A. Brief Review of Fuzzy Set Theory. Introduction. Fuzzy Sets versus Crisp Sets. The Shape of Fuzzy Sets. Fuzzy Sets Operations. Fuzzy Logic and Approximate Reasoning. Problems. Apprendix B. The Fuzzy Logic Development Kit. Introduction. Description of the FULDEK Program. EDITOR Option. The RUN Option. Post-Processing Feature of FULDEK. A Real-Time Laser Beam Fuzzy Controller. New Options in Version 4.0 of the FULDEK Program. Conclusion. References. Index.


Proceedings ArticleDOI
11 Dec 1996
TL;DR: A /spl Delta/~-composition is generated from Mizumoto's (1983) /spl forall//spl dot/-A/spl I.udot/-composition, and an algorithm using Mandani's fuzzy implication (R) is proposed to describe the system operation.
Abstract: A /spl Delta/~-composition (i.e. /spl forall//spl dot/-A/spl I.udot/-composition) is generated from Mizumoto's (1983) /spl Delta/-composition, and an algorithm using Mandani's fuzzy implication (R) is proposed to describe the system operation. Computer simulation is performed on Box and Jenkin's (1970) gas furnace data. The identified fuzzy model is compared with Zadeh's max-min algorithm.

Journal ArticleDOI
TL;DR: The main contribution of this paper is the development of an equivalent principle; that is, the design of a fuzzy control system is equivalent to thedesign of a set of linear time-invariant ‘extreme’ systems.
Abstract: This paper presents a design method for a class of fuzzy control systems. The class of fuzzy systems considered can be represented by the Takagi-Sugeno fuzzy model which is a type of dynamic fuzzy model. A constructive algorithm is developed to obtain the stabilizing feedback control law for the system. The main contribution of this paper is the development of an equivalent principle; that is, the design of a fuzzy control system is equivalent to the design of a set of linear time-invariant ‘extreme’ systems. Thus any design method in linear control system theory can be used to design a fuzzy control system. An example is given to illustrate the application of the method.