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BL
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| Record Number
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862067
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| Main Entry
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Qi, Ruiyun
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| Title & Author
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Fuzzy System Identification and Adaptive Control /\ Ruiyun Qi, Gang Tao, Bin Jiang.
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| Publication Statement
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Cham, Switzerlad :: Springer,, [2019]
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| Series Statement
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Communications and Control Engineering
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| Page. NO
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1 online resource (293 pages)
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| ISBN
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3030198820
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: 9783030198824
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3030198812
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9783030198817
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; Contents; 1 Introduction; 1.1 Basic Concepts of Fuzzy Systems; 1.1.1 Fuzzy Sets; 1.1.2 Fuzzy Logic Operations; 1.1.3 Fuzzy Inference System; 1.2 Typical Fuzzy Systems; 1.2.1 Mamdani Fuzzy Systems; 1.2.2 Takagi-Sugeno (T-S) Fuzzy Systems; 1.3 Fuzzy System Identification; 1.4 Fuzzy System Based Adaptive Control; 1.4.1 Fuzzy Systems as Static Function Approximators; 1.4.2 Fuzzy Systems as Dynamic Systems; 1.5 What This Book Is About; References; 2 T-S Fuzzy Systems; 2.1 Static T-S Fuzzy Systems; 2.2 Dynamic T-S Fuzzy Systems; 2.2.1 Continuous-Time State-Space Form
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2.2.2 Discrete-Time State-Space Form2.2.3 Discrete-Time Input-Output Form; 2.3 Universal Approximation Property; 2.4 Stability and Stabilization Control; 2.4.1 Stability of T-S Fuzzy Systems; 2.4.2 Stabilization Control of T-S Fuzzy Systems; 2.5 Tracking Control of T-S Fuzzy Systems; 2.5.1 State Tracking Control; 2.5.2 Output Tracking Control; 2.6 Summary; References; 3 Adaptive Control: A Tutorial Introduction; 3.1 Adaptive Linear Control; 3.1.1 Indirect Adaptive Control; 3.1.2 Direct Adaptive Control; 3.1.3 Model Reference Adaptive Control; 3.1.4 Discrete-Time Adaptive Linear Control
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3.2 Adaptive Nonlinear Control3.2.1 A Continuous-Time Design Example; 3.2.2 A Discrete-Time Design Example; 3.3 Summary; References; 4 T-S Fuzzy System Identification Using I/O Data; 4.1 Introduction; 4.2 Offline Identification of T-S Fuzzy Systems; 4.2.1 Identification of Premise Variables; 4.2.2 Identification of Number of Rules; 4.2.3 Estimation of Consequent Parameters; 4.2.4 Adjustment of Membership Parameters; 4.2.5 Procedure for Offline Identification; 4.2.6 Simulation Study; 4.3 Online Identification of T-S Fuzzy Systems; 4.3.1 Online Fuzzy Clustering Algorithm
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4.3.2 Estimation of Consequent Parameters4.3.3 Procedure for Online Identification; 4.3.4 Simulation Study; 4.4 Summary; References; 5 Adaptive T-S Fuzzy State Tracking Control Using State Feedback; 5.1 Problem Statement; 5.2 Design for T-S Fuzzy Systems in Canonical Form; 5.2.1 Plant Model and Reference System; 5.2.2 Nominal Controller and Matching Conditions; 5.2.3 Adaptive Control Scheme; 5.3 Design for T-S Fuzzy Systems in General Form (m leqn); 5.3.1 Plant Model and Reference System; 5.3.2 Nominal Controller and Matching Conditions; 5.3.3 Adaptive Control Scheme
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| Abstract
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This book provides readers with a systematic and unified framework for identification and adaptive control of Takagi-Sugeno (T-S) fuzzy systems. Its design techniques help readers applying these powerful tools to solve challenging nonlinear control problems. The book embodies a systematic study of fuzzy system identification and control problems, using T-S fuzzy system tools for both function approximation and feedback control of nonlinear systems. Alongside this framework, the book also: introduces basic concepts of fuzzy sets, logic and inference system; discusses important properties of T-S fuzzy systems; develops offline and online identification algorithms for T-S fuzzy systems; investigates the various controller structures and corresponding design conditions for adaptive control of continuous-time T-S fuzzy systems; develops adaptive control algorithms for discrete-time input-output form T-S fuzzy systems with much relaxed design conditions, and discrete-time state-space T-S fuzzy systems; and designs stable parameter-adaptation algorithms for both linearly and nonlinearly parameterized T-S fuzzy systems. The authors address adaptive fault compensation problems for T-S fuzzy systems subject to actuator faults. They cover a broad spectrum of related technical topics and to develop a substantial set of adaptive nonlinear system control tools. Fuzzy System Identification and Adaptive Control helps engineers in the mechanical, electrical and aerospace fields, to solve complex control design problems. The book can be used as a reference for researchers and academics in nonlinear, intelligent, adaptive and fault-tolerant control.
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| Subject
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Adaptive control systems.
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| Subject
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Fuzzy systems.
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| Subject
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Adaptive control systems.
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| Subject
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Fuzzy systems.
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| Dewey Classification
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629.8
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| LC Classification
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TJ217.G5 2019
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| Added Entry
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Jiang, Bin
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Tao, Gang
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