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Stable adaptive control and estimation for nonlinear systems : neural and fuzzy approximator techniques /

A powerful, yet easy-to-use design methodology for the control of nonlinear dynamic systems. A key issue in the design of control systems is proving that the resulting closed-loop system is stable, especially in cases of high consequence applications, where process variations or failure could result...

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Bibliographic Details
Other Authors: Spooner, Jeffrey T.
Format: eBook
Language:English
Published: New York : Wiley, ©2002.
Series:Adaptive and learning systems for signal processing, communications, and control.
Subjects:
Nonlinear control theory.
Adaptive control systems.
Fuzzy logic.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Stability and Robustness
  • Adaptive Control: Techniques and Properties
  • Indirect Adaptive Control Schemes
  • Direct Adaptive Control Schemes
  • The Role of Neural Networks and Fuzzy Systems
  • Approximator Structures and Properties
  • Benefits for Use in Adaptive Systems
  • Mathematical Foundations
  • Vectors, Matrices, and Signals: Norms and Properties
  • Vectors
  • Matrices
  • Signals
  • Functions: Continuity and Convergence
  • Continuity and Differentiation
  • Convergence
  • Characterizations of Stability and Boundedness
  • Stability Definitions
  • Boundedness Definitions
  • Lyapunov's Direct Method
  • Preliminaries: Function Properties
  • Conditions for Stability
  • Conditions for Boundedness
  • Input-to-State Stability
  • Input-to-State Stability Definitions
  • Conditions for Input-to-State Stability
  • Special Classes of Systems
  • Autonomous Systems
  • Linear Time-Invariant Systems
  • Neural Networks and Fuzzy Systems
  • Neural Networks
  • Neuron Input Mappings
  • Neuron Activation Functions
  • The Mulitlayer Perceptron
  • Radial Basis Neural Network
  • Tapped Delay Neural Network
  • Fuzzy Systems
  • Rule-Base and Fuzzification
  • Inference and Defuzzification
  • Takagi-Sugeno Fuzzy Systems
  • Optimization for Training Approximators
  • Problem Formulation
  • Linear Least Squares
  • Batch Least Squares
  • Recursive Least Squares
  • Nonlinear Least Squares
  • Gradient Optimization: Single Training Data Pair
  • Gradient Optimization: Multiple Training Data Pairs
  • Discrete Time Gradient Updates.