Titre :	Systems and Control
Type de document :	texte imprimé
Auteurs :	Stanislaw H. Zak, Auteur
Editeur :	New york : Oxford University Press Inc
Année de publication :	2003
Collection :	The Oxford Series in Electrical and Computer Engineering
Importance :	704 p.
Présentation :	couv. ill. en coul., ill.
Format :	24,1 cm.
ISBN/ISSN/EAN :	978-0-19-515011-7
Langues :	Anglais (eng)
Index. décimale :	25-04 Théorie des systèmes:systèmes asservis
Résumé :	Systems and Control presents modeling, analysis, and control of dynamical systems. Introducing students to the basics of dynamical system theory and supplying them with the tools necessary for control system design, it emphasizes design and demonstrates how dynamical system theory fits into practical applications. Classical methods and the techniques of postmodern control engineering are presented in a unified fashion, demonstrating how the current tools of a control engineer can supplement more classical tools.Broad in scope, Systems and Control shows the multidisciplinary role of dynamics and control; presents neural networks, fuzzy systems, and genetic algorithms; and provides a self-contained introduction to chaotic systems. The text employs Lyapunov's stability theory as a unifying medium for different types of dynamical systems, using it--with its variants--to analyze dynamical system models. Specifically, optimal, fuzzy, sliding mode, and chaotic controllers are all constructed with the aid of the Lyapunov method and its extensions. In addition, a class of neural networks is also analyzed using Lyapunov's method.Ideal for advanced undergraduate and beginning graduate courses in systems and control, this text can also be used for introductory courses in nonlinear systems and modern automatic control. It requires working knowledge of basic differential equations and elements of linear algebra; a review of the necessary mathematical techniques and terminology is provided.
Note de contenu :	Contents 1 Dynamical Systems and Modeling 1.1 What Is a System? 1.2 Open-Loop Versus Closed-Loop 1.3 Axiomatic Definition of a Dynamical System 1.4 Mathematical Modeling 1.5 Review of Work and Energy Concepts 1.6 The Lagrange Equations of Motion 1.7 Modeling Examples 2 Analysis of Modeling Equations 2.1 State-Plane Analysis 2.2 Numerical Techniques 2.3 Principles of Linearization 2.4 Linearizing Differential Equations 2.5 Describing Function Method 3 Linear Systems 3.1 Reachability and Controllability 3.2 Observability and Constructability 3.3 Companion Forms 3.4 Linear State-Feedback Control 3.5 State Estimators 3.6 Combined Controller–Estimator Compensator 4 Stability 4.1 Informal Introduction to Stability 4.2 Basic Definitions of Stability 4.3 Stability of Linear Systems 4.4 Evaluating Quadratic Indices 4.5 Discrete-Time Lyapunov Equation 4.6 Constructing Robust Linear Controllers 4.7 Hurwitz and Routh Stability Criteria 4.8 Stability of Nonlinear Systems 4.9 Lyapunov’s Indirect Method 4.10 Discontinuous Robust Controllers 4.11 Uniform Ultimate Boundedness 4.12 Lyapunov-Like Analysis 4.13 LaSalle’s Invariance Principle 5 Optimal Control 5.1 Performance Indices 5.2 A Glimpse at the Calculus of Variations 5.3 Linear Quadratic Regulator 5.4 Dynamic Programming 5.5 Pontryagin’s Minimum Principle 6 Sliding Modes 6.1 Simple Variable Structure Systems 6.2 Sliding Mode Definition 6.3 A Simple Sliding Mode Controller 6.4 Sliding in Multi-Input Systems 6.5 Sliding Mode and System Zeros 6.6 Nonideal Sliding Mode 6.7 Sliding Surface Design 6.8 State Estimation of Uncertain Systems 6.9 Sliding Modes in Solving Optimization Problems 7 Vector Field Methods 7.1 A Nonlinear Plant Model 7.2 Controller Form 7.3 Linearizing State-Feedback Control 7.4 Observer Form 7.5 Asymptotic State Estimator 7.6 Combined Controller–Estimator Compensator 8 Fuzzy Systems 8.1 Motivation and Basic Definitions 8.2 Fuzzy Arithmetic and Fuzzy Relations 8.3 Standard Additive Model 8.4 Fuzzy Logic Control 8.5 Stabilization Using Fuzzy Models 8.6 Stability of Discrete Fuzzy Models 8.7 Fuzzy Estimator 8.8 Adaptive Fuzzy Control 9 Neural Networks 9.1 Threshold Logic Unit 9.2 Identification Using Adaptive Linear Element 9.3 Backpropagation 9.4 Neural Fuzzy Identifier 9.5 Radial-Basis Function (RBF) Networks 9.6 A Self-Organizing Network 9.7 Hopfield Neural Network 9.8 Hopfield Network Stability Analysis 9.9 Brain-State-in-a-Box (BSB) Models 10 Genetic and Evolutionary Algorithms 10.1 Genetics as an Inspiration for an Optimization Approach 10.2 Implementing a Canonical Genetic Algorithm 10.3 Analysis of the Canonical Genetic Algorithm 10.4 Simple Evolutionary Algorithm (EA) 10.5 Evolutionary Fuzzy Logic Controllers 11 Chaotic Systems and Fractals 11.1 Chaotic Systems Are Dynamical Systems with Wild Behavior 11.2 Chaotic Behavior of the Logistic Equation 11.3 Fractals 11.4 Lyapunov Exponents 11.5 Discretization Chaos 11.6 Controlling Chaotic Systems Appendix: Math Review -Index

Systems and Control [texte imprimé] / Stanislaw H. Zak, Auteur . - New york : Oxford University Press Inc, 2003 . - 704 p. : couv. ill. en coul., ill. ; 24,1 cm.. - (The Oxford Series in Electrical and Computer Engineering) .
ISBN : 978-0-19-515011-7
Langues : Anglais (eng)

Index. décimale :	25-04 Théorie des systèmes:systèmes asservis
Résumé :	Systems and Control presents modeling, analysis, and control of dynamical systems. Introducing students to the basics of dynamical system theory and supplying them with the tools necessary for control system design, it emphasizes design and demonstrates how dynamical system theory fits into practical applications. Classical methods and the techniques of postmodern control engineering are presented in a unified fashion, demonstrating how the current tools of a control engineer can supplement more classical tools.Broad in scope, Systems and Control shows the multidisciplinary role of dynamics and control; presents neural networks, fuzzy systems, and genetic algorithms; and provides a self-contained introduction to chaotic systems. The text employs Lyapunov's stability theory as a unifying medium for different types of dynamical systems, using it--with its variants--to analyze dynamical system models. Specifically, optimal, fuzzy, sliding mode, and chaotic controllers are all constructed with the aid of the Lyapunov method and its extensions. In addition, a class of neural networks is also analyzed using Lyapunov's method.Ideal for advanced undergraduate and beginning graduate courses in systems and control, this text can also be used for introductory courses in nonlinear systems and modern automatic control. It requires working knowledge of basic differential equations and elements of linear algebra; a review of the necessary mathematical techniques and terminology is provided.
Note de contenu :	Contents 1 Dynamical Systems and Modeling 1.1 What Is a System? 1.2 Open-Loop Versus Closed-Loop 1.3 Axiomatic Definition of a Dynamical System 1.4 Mathematical Modeling 1.5 Review of Work and Energy Concepts 1.6 The Lagrange Equations of Motion 1.7 Modeling Examples 2 Analysis of Modeling Equations 2.1 State-Plane Analysis 2.2 Numerical Techniques 2.3 Principles of Linearization 2.4 Linearizing Differential Equations 2.5 Describing Function Method 3 Linear Systems 3.1 Reachability and Controllability 3.2 Observability and Constructability 3.3 Companion Forms 3.4 Linear State-Feedback Control 3.5 State Estimators 3.6 Combined Controller–Estimator Compensator 4 Stability 4.1 Informal Introduction to Stability 4.2 Basic Definitions of Stability 4.3 Stability of Linear Systems 4.4 Evaluating Quadratic Indices 4.5 Discrete-Time Lyapunov Equation 4.6 Constructing Robust Linear Controllers 4.7 Hurwitz and Routh Stability Criteria 4.8 Stability of Nonlinear Systems 4.9 Lyapunov’s Indirect Method 4.10 Discontinuous Robust Controllers 4.11 Uniform Ultimate Boundedness 4.12 Lyapunov-Like Analysis 4.13 LaSalle’s Invariance Principle 5 Optimal Control 5.1 Performance Indices 5.2 A Glimpse at the Calculus of Variations 5.3 Linear Quadratic Regulator 5.4 Dynamic Programming 5.5 Pontryagin’s Minimum Principle 6 Sliding Modes 6.1 Simple Variable Structure Systems 6.2 Sliding Mode Definition 6.3 A Simple Sliding Mode Controller 6.4 Sliding in Multi-Input Systems 6.5 Sliding Mode and System Zeros 6.6 Nonideal Sliding Mode 6.7 Sliding Surface Design 6.8 State Estimation of Uncertain Systems 6.9 Sliding Modes in Solving Optimization Problems 7 Vector Field Methods 7.1 A Nonlinear Plant Model 7.2 Controller Form 7.3 Linearizing State-Feedback Control 7.4 Observer Form 7.5 Asymptotic State Estimator 7.6 Combined Controller–Estimator Compensator 8 Fuzzy Systems 8.1 Motivation and Basic Definitions 8.2 Fuzzy Arithmetic and Fuzzy Relations 8.3 Standard Additive Model 8.4 Fuzzy Logic Control 8.5 Stabilization Using Fuzzy Models 8.6 Stability of Discrete Fuzzy Models 8.7 Fuzzy Estimator 8.8 Adaptive Fuzzy Control 9 Neural Networks 9.1 Threshold Logic Unit 9.2 Identification Using Adaptive Linear Element 9.3 Backpropagation 9.4 Neural Fuzzy Identifier 9.5 Radial-Basis Function (RBF) Networks 9.6 A Self-Organizing Network 9.7 Hopfield Neural Network 9.8 Hopfield Network Stability Analysis 9.9 Brain-State-in-a-Box (BSB) Models 10 Genetic and Evolutionary Algorithms 10.1 Genetics as an Inspiration for an Optimization Approach 10.2 Implementing a Canonical Genetic Algorithm 10.3 Analysis of the Canonical Genetic Algorithm 10.4 Simple Evolutionary Algorithm (EA) 10.5 Evolutionary Fuzzy Logic Controllers 11 Chaotic Systems and Fractals 11.1 Chaotic Systems Are Dynamical Systems with Wild Behavior 11.2 Chaotic Behavior of the Logistic Equation 11.3 Fractals 11.4 Lyapunov Exponents 11.5 Discretization Chaos 11.6 Controlling Chaotic Systems Appendix: Math Review -Index