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BL
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| Record Number
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889866
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| Main Entry
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Sharma, Kaushik Das
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| Title & Author
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Intelligent control : : a stochastic optimization based adaptive fuzzy approach /\ Kaushik Das Sharma, Amitava Chatterjee, Anjan Rakshit.
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| Publication Statement
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Singapore :: Springer,, [2018]
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, ©2018
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| Series Statement
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Cognitive intelligence and robotics
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| Page. NO
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1 online resource :: illustrations (some color)
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| ISBN
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9789811312984
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: 9811312982
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9789811312977
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9811312974
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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; About the Authors; Prologue; 1 Intelligent Adaptive Fuzzy Control; 1.1 Way to Modern Control Theory; 1.2 Overview of Fuzzy Control; 1.3 Overview of Adaptive Fuzzy Control; 1.4 Stability Issues in Adaptive Fuzzy Control; 1.5 Intelligent Adaptive Fuzzy Control; 1.6 State of the Art; 1.7 Summary; References; 2 Some Contemporary Stochastic Optimization Algorithms: A Glimpse; 2.1 Introduction; 2.2 Genetic Algorithm (GA); 2.3 Particle Swarm Optimization (PSO); 2.4 Covariance Matrix Adaptation (CMA); 2.5 Harmony Search Algorithm (HSA); 2.6 Summary; References
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3 Fuzzy Controller Design I: Stochastic Algorithm-Based Approach3.1 Introduction; 3.2 Stable Fuzzy Controllers; 3.3 PSO-Based Design Methodology; 3.3.1 PSO-Based Fuzzy Controller Design; 3.3.2 The Design Algorithm; 3.4 HSA-Based Design Methodology; 3.4.1 HSA-Based Fuzzy Controller Design; 3.4.2 The Design Algorithm; 3.5 Stochastic Algorithm-Based Design Methodology: A Generalized Approach; 3.5.1 The Generic Design Algorithm; 3.6 A Comparative Case Study of Different Algorithms; 3.6.1 Case Study I: DC Motor Containing Nonlinear Friction Characteristics; 3.6.1.1 Performance Analysis of PSOBA
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3.6.1.2 Performance Analysis of HSABA3.6.2 Case Study II: Duffing's Oscillatory System; 3.6.2.1 Case Study II (A): Duffing's Oscillatory System; Performance Analysis of PSOBA; Performance Analysis of HSABA; 3.6.2.2 Case Study II (B): Duffing's Oscillatory System with Disturbance; Performance Analysis of PSOBA; Performance Analysis of HSABA; 3.7 Summary; References; Lyapunov Strategy Based Design Methodologies; 4 Fuzzy Controller Design II: Lyapunov Strategy-Based Adaptive Approach; 4.1 Introduction; 4.2 Stable Adaptive Fuzzy Controllers; 4.3 Lyapunov Strategy-Based Approach (LSBA)
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4.3.1 The Design Algorithm4.4 Case Studies; 4.4.1 Case Study I: DC Motor Containing Nonlinear Friction Characteristics; 4.4.2 Case Study II: Duffing's Oscillatory System; 4.4.2.1 Case Study II(A): Duffing's Oscillatory System; 4.4.2.2 Case Study II(B): Duffing's Oscillatory System with Disturbance; 4.5 Summary; References; 5 Fuzzy Controller Design III: Hybrid Adaptive Approaches; 5.1 Introduction; 5.2 Hybrid Design Methodology Using Lyapunov Strategy; 5.2.1 Lyapunov Theory-Based Hybrid Cascade Model; 5.2.1.1 Lyapunov Theory-Based Hybrid Cascade Algorithm
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5.2.2 Lyapunov Theory-Based Hybrid Concurrent Model5.2.2.1 Lyapunov Theory-Based Hybrid Concurrent Algorithm; 5.2.3 Lyapunov Theory-Based Hybrid Preferential Model; 5.2.3.1 Lyapunov Theory-Based Hybrid Preferential Algorithm; 5.3 Case Studies; 5.3.1 Case Study I: DC Motor Containing Nonlinear Friction Characteristics; 5.3.1.1 Performance Analysis of PSO-Based Hybrid Controllers; 5.3.1.2 Performance Analysis of HSA-Based Hybrid Controllers; 5.3.2 Case Study II: Duffing's Oscillatory System; 5.3.2.1 Case Study II(A): Duffing's Oscillatory System
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| Abstract
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This book discusses systematic designs of stable adaptive fuzzy logic controllers employing hybridizations of Lyapunov strategy-based approaches/H? theory-based approaches and contemporary stochastic optimization techniques. The text demonstrates how candidate stochastic optimization techniques like Particle swarm optimization (PSO), harmony search (HS) algorithms, covariance matrix adaptation (CMA) etc. can be utilized in conjunction with the Lyapunov theory/H? theory to develop such hybrid control strategies. The goal of developing a series of such hybridization processes is to combine the strengths of both Lyapunov theory/H?theory-based local search methods and stochastic optimization-based global search methods, so as to attain superior control algorithms that can simultaneously achieve desired asymptotic performance and provide improved transient responses. The book also demonstrates how these intelligent adaptive control algorithms can be effectively utilized in real-life applications such as in temperature control for air heater systems with transportation delay, vision-based navigation of mobile robots, intelligent control of robot manipulators etc.
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| Subject
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Fuzzy systems.
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Intelligent control systems.
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Stochastic processes.
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Artificial intelligence.
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Automatic control engineering.
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Fuzzy systems.
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Image processing.
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Intelligent control systems.
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Optimization.
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Probability statistics.
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Stochastic processes.
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| Subject
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TECHNOLOGY ENGINEERING-- Engineering (General)
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| Dewey Classification
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629.8
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| LC Classification
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TJ217.5.S53 2018eb
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| Added Entry
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Chatterjee, Amitava
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Rakshit, Anjan
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