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Table of Contents
Intro
Preface
Contents
1 An Introduction to Fuzzy and Fuzzy Control Systems
1.1 Historical Background
1.2 What is Adaptive Fuzzy Control?
1.3 Why Adaptive Fuzzy Control?
1.4 Problems in Adaptive Fuzzy Controller
References
2 Classification of Adaptive Fuzzy Controllers
2.1 Direct Adaptive Fuzzy Controller
2.2 Indirect Adaptive Fuzzy Controller
2.3 Integrating Adaptive Fuzzy Controller with Other Controllers
2.3.1 Integrating Direct and Indirect Adaptive Controllers
2.3.2 Integrating Hybrid Fuzzy Controller with Other Controllers to Compensate for Estimation Error
2.3.3 Integrating Hybrid Fuzzy Controller with Output Feedback Controller
2.3.4 Integrating Adaptive Fuzzy Controller with Hinfty Control
2.3.5 Integrating Adaptive Fuzzy Controller with Supervised Controller
2.3.6 Integrating Adaptive Fuzzy Controller with Other Control Methods
2.4 Different Classes of Nonlinear Systems
2.4.1 Affine Nonlinear Systems
2.4.2 Non-affine Nonlinear Systems
2.4.3 Nonlinear Feedback Systems
2.4.4 Nonlinear Pure-Feedback Systems
2.4.5 Nonlinear Single-Input-Single-Output and Multi-Input-Multi-Output Systems
2.4.6 Nonlinear Output and State Feedback Systems
2.4.7 Discrete and Continuous Systems
2.5 Adaptation Mechanism in Fuzzy Systems
2.5.1 Setting Parameters
2.5.2 Setting Structure and Parameter
2.6 Conclusion
References
3 Type-2 Fuzzy Systems
3.1 Introduction
3.2 Singleton Fuzzy Systems
3.3 Non-singleton Fuzzy Systems
3.4 Features of Type-2 Fuzzy Systems
3.5 Basic Operations in Type-2 Fuzzy
3.6 Fuzzification
3.7 Rules
3.8 Logics
3.9 Type Reduction
3.10 Implementation in MATLAB
3.11 Designing a General Type-2 Fuzzy System with an Example
3.12 Interval Type-2 Fuzzy System
3.13 Conclusion
References
4 Training Interval Type-2 Fuzzy Systems Based on Error Backpropagation
4.1 Introduction
4.2 Training Fuzzy Systems with Nie-Tan Type-Reduction
4.2.1 Implementation in MATLAB
4.3 Fuzzy System with KM-EKM Type-Reduction
4.4 Training Type-2 Fuzzy System with Extended Kalman Filter
4.5 Training Type-2 Fuzzy System Based on Genetic Algorithm
4.5.1 Introduction
4.6 Calling Genetic Algorithm
4.7 Jargons of GA Toolkit in MATLAB
4.7.1 GA-Based Optimization of Neuro-Fuzzy System Parameters
4.8 Training Neural Networks Based on PSO
4.8.1 Introduction
4.9 Formulation of Algorithm
4.10 Implementation in MATLAB
4.11 Training Type-2 Fuzzy System Through Second-Order Algorithms
4.11.1 Introduction
4.11.2 Newton's Method
4.11.3 Levenberg-Marquardt Algorithm
4.11.4 Conjugate Gradient Method
4.11.5 Implementation in MATLAB
4.12 Conclusion
References
5 Baseline Indirect Adaptive Control
5.1 Problem Specifications
5.2 Designing Fuzzy Controller
Preface
Contents
1 An Introduction to Fuzzy and Fuzzy Control Systems
1.1 Historical Background
1.2 What is Adaptive Fuzzy Control?
1.3 Why Adaptive Fuzzy Control?
1.4 Problems in Adaptive Fuzzy Controller
References
2 Classification of Adaptive Fuzzy Controllers
2.1 Direct Adaptive Fuzzy Controller
2.2 Indirect Adaptive Fuzzy Controller
2.3 Integrating Adaptive Fuzzy Controller with Other Controllers
2.3.1 Integrating Direct and Indirect Adaptive Controllers
2.3.2 Integrating Hybrid Fuzzy Controller with Other Controllers to Compensate for Estimation Error
2.3.3 Integrating Hybrid Fuzzy Controller with Output Feedback Controller
2.3.4 Integrating Adaptive Fuzzy Controller with Hinfty Control
2.3.5 Integrating Adaptive Fuzzy Controller with Supervised Controller
2.3.6 Integrating Adaptive Fuzzy Controller with Other Control Methods
2.4 Different Classes of Nonlinear Systems
2.4.1 Affine Nonlinear Systems
2.4.2 Non-affine Nonlinear Systems
2.4.3 Nonlinear Feedback Systems
2.4.4 Nonlinear Pure-Feedback Systems
2.4.5 Nonlinear Single-Input-Single-Output and Multi-Input-Multi-Output Systems
2.4.6 Nonlinear Output and State Feedback Systems
2.4.7 Discrete and Continuous Systems
2.5 Adaptation Mechanism in Fuzzy Systems
2.5.1 Setting Parameters
2.5.2 Setting Structure and Parameter
2.6 Conclusion
References
3 Type-2 Fuzzy Systems
3.1 Introduction
3.2 Singleton Fuzzy Systems
3.3 Non-singleton Fuzzy Systems
3.4 Features of Type-2 Fuzzy Systems
3.5 Basic Operations in Type-2 Fuzzy
3.6 Fuzzification
3.7 Rules
3.8 Logics
3.9 Type Reduction
3.10 Implementation in MATLAB
3.11 Designing a General Type-2 Fuzzy System with an Example
3.12 Interval Type-2 Fuzzy System
3.13 Conclusion
References
4 Training Interval Type-2 Fuzzy Systems Based on Error Backpropagation
4.1 Introduction
4.2 Training Fuzzy Systems with Nie-Tan Type-Reduction
4.2.1 Implementation in MATLAB
4.3 Fuzzy System with KM-EKM Type-Reduction
4.4 Training Type-2 Fuzzy System with Extended Kalman Filter
4.5 Training Type-2 Fuzzy System Based on Genetic Algorithm
4.5.1 Introduction
4.6 Calling Genetic Algorithm
4.7 Jargons of GA Toolkit in MATLAB
4.7.1 GA-Based Optimization of Neuro-Fuzzy System Parameters
4.8 Training Neural Networks Based on PSO
4.8.1 Introduction
4.9 Formulation of Algorithm
4.10 Implementation in MATLAB
4.11 Training Type-2 Fuzzy System Through Second-Order Algorithms
4.11.1 Introduction
4.11.2 Newton's Method
4.11.3 Levenberg-Marquardt Algorithm
4.11.4 Conjugate Gradient Method
4.11.5 Implementation in MATLAB
4.12 Conclusion
References
5 Baseline Indirect Adaptive Control
5.1 Problem Specifications
5.2 Designing Fuzzy Controller