CHAPTER 1 Introduction

1．1 Overview

1．2 Conventional Control System Design

1．2．1 Mathematical Modeling

1．2．2 Performance Objectives and Design Constraints

1．2．3 Controller Design

1．2．4 Performance Evaluation

1．3 Fuzzy Control System Design

1．3．1 Modeling Lssues and Performance Objectives

1．3．2 Fuzzy Controller Design

1．3．3 Performance Evaluation

1．3．4 Application Areas

1．4 What This Book Is About

1．4．1 What the Techniques Are Good For:An Example

1．4．2 Objectives of This Book

1．5 Summary

1．6 For Further Study

1．7 Exercises

CHAPTER 2 Fuzzy Control:The Basics

2．1 Overview

2．2 Fuzzy Control: A Tutorial Introduction

2．2．1 Choosing Fuzzy Controller Inputs and Outputs

2．2．2 Putting Control Knowledge into Rule-Bases

2．2．3 Fuzzy Quantification of Knowledge

2．2．4 Matching: Determining Which Rules to Use

2．2．5 Inference Step:Determining Conclusions

2．2．6 Converting Decisions into Actions

2．2．7 Graphical Depiction of Fuzzy Decision Making

2．2．8 Visualizing the Fuzzy Controller s Dynamical Operation

2．3 General Fuzzy Systems

2．3．1 Linguistic Variables, Values, and Rules

2．3．2 Fuzzy Sets, Fuzzy Logic, and the Rule-Base

2．3．3 Fuzzification

2．3．4 The Inference Mechanism

2．3．5 Defuzzification

2．3．6 Mathematical Representations of Fuzzy Systems

2．3．7 Takagi-Sugeno Fuzzy Systems

2．3．8 Fuzzy Systems Are Universal Approximators

2．4 Simple Design Example: The Inverted Pendulum

2．4．1 Tuning via Scaling Universes of Discourse

2．4．2 Tuning Membership Functions

2．4．3 The Nonlinear Surface for the Fuzzy Controller

2．4．4 Summary:Basic Design Guidelines

2．5 Simulation of Fuzzy Control Systems

2．5．1 Simulation of Nonlinear Systems

2．5．2 Fuzzy Controller Arrays and Subroutines

2．5．3 Fuzzy Controller Pseudocode

2．6 Real-Time Implementation Issues

2．6．1 Computation Time

2．6．2 Memory Requirements

2．7 Summary

2．8 For Further Study

2．9 Exercises

2．10 Design Problems

CHAPTER 3 Case Studies in Design and Implementation

3．1 Overview

3．2 Design Methodology

3．3 Vibration Damping for a Flexible Robot

3．3．1 The Two-Link Flexible Robot

3．3．2 Uncoupled Direct Fuzzy Control

3．3．3 Coupled Direct Fuzzy Control

3．4 Balancing a Rotational Inverted Pendulum

3．4．1 The Rotational Inverted Pendulum

3．4．2 A Conventional Approach to Balancing Control

3．4．3 Fuzzy Control for Balancing

3．5 Machine Scheduling

3．5．1 Conventional Scheduling Policies

3．5．2 Fuzzy Scheduler for a Single Machine

3．5．3 Fuzzy Versus Conventional Schedulers

3．6 Fuzzy Decision-Making Systems

3．6．1 Infectious Disease Warning System

3．6．2 Failure Warning System for an Aircraft

3．7 Summary

3．8 For Further Study

3．9 Exercises

3．10 Design Problems

CHAPTER 4 Nonlinear Analysls

4．1 Overview

4．2 Parameterized Fuzzy Controllers

4．2．1 Proportional Fuzzy Controller

4．2．2 Proportional-Derivative Fuzzy Controller

4．3 Lyapunov Stability Analysis

4．3．1 Mathematical Preliminaries

4．3．2 Lyapunov s Direct Method

4．3．3 Lyapunov s Indirect Method

4．3．4 Example:Inverted Pendulum

4．3．5 Example: The Parallel Distributed Compensator

4．4 Absolute Stability and the Circle Criterion

4．4．1 Analysis of Absolute Stability

4．4．2 Example:Temperature Control

4．5 Analysis of Steady-State Tracking Error

4．5．1 Theory of Tracking Error for Nonlinear Systems

4．5．2 Example: Hydrofoil Controller Design

4．6 Describing Function Analysis

4．6．1 Predicting the Existence and Stability of Llmit Cycles

4．6．2 SISO Example: Underwater Vehicle Control System

4．6．3 MISO Example: Tape Drive Servo

4．7 Limitations of the Theory

4．8 Summary

4．9 For Further Study

4．10 Exercises

4．11 Design Problems

CHAPTER 5 Fuzzy Identification and Estimation

5．1 Overview

5．2 Fitting Functions to Data

5．2．1 The Function Approximation Problem

5．2．2 Relation to Identification, Estimation, and Prediction

5．2．3 Choosing the Data Set

5．2．4 Incorporating Linguistic Information

5．2．5 Case Study: Engine Failure Data Sets

5．3 Least Squares Methods

5．3．1 Batch Least Squares

5．3．2 Recursive Least Squares

5．3．3 Tuning Fuzzy Systems

5．3．4 Example: Batch Least Squares Training of Fuzzy Systems

5．3．5 Example: Recursive Least Squares Training of Fuzzy Systems

5．4 Gradient Methods

5．4．1 Training Standard Fuzzy Systems

5．4．2 Implementation Issues and Example

5．4．3 Training Takagi-Sugeno Fuzzy Systems

5．4．4 Momentum Term and Step Size

5．4．5 Newton and Gauss-Newton Methods

5．5 Clustering Methods

5．5．1 Clustering with Optimal Output Predefuzzification

5．5．2 Nearest Neighborhood Clustering

5．6 Extracting Rules from Data

5．6．1 Learning from Examples（LFE）

5．6．2 Modified Learning from Examples（MLFE）

5．7 Hybrid Methods

5．8 Case Study: FDI for an Engine

5．8．1 Experimental Engine and Testing Conditions

5．8．2 Fuzzy Estimator Construction and Results

5．8．3 Failure Detection and Identification（FDI）Strategy

5．9 Summary

1．10 For Further Study

5．11 Exercises

5．12 Design Problems

CHAPTER 6 Adaptive Fuzzy Control

6．1 Overview

6．2 Fuzzy Model Reference Learning Control（FMRLC）

6．2．1 The Fuzzy Controller

6．2．2 The Reference Model

6．2．3 The Learning Mechanism

6．2．4 Alternative Knowledge-Base Modifiers

6．2．5 Design Guidelines for the Fuzzy Inverse Model

6．3 FMRLC: Design and Implementation Case Studies

6．3．1 Cargo Ship Steering

6．3．2 Fault-Tolerant Aircraft Control

6．3．3 Vibration Damping for a Flexible Robot

6．4 Dynamically Focused Learning（DFL）

6．4．1 Magnetic Ball Suspension System: Motivation for DFL

6．4．2 Auto-Tuning Mechanism

6．4．3 Auto-Attentive Mechanism

6．4．4 Auto-Attentive Mechanism with Memory

6．5 DFL:Design and Implementation Case Studies

6．5．1 Rotational Inverted Pendulum

6．5．2 Adaptive Machine Scheduling

6．6 Indirect Adaptive Fuzzy Control

6．6．1 On-Line Identification Methods

6．6．2 Adaptive Control for Feedback Linearizable Systems

6．6．3 Adaptive Parallel Distributed Compensation

6．6．4 Example: Level Control in a Surge Tank

6．7 Summary

6．8 For Further Study

6．9 Exercises

6．10 Design Problems

CHAPTER 7 Fuzzy Supervlsory Control

7．1 Overview

7．2 Supervision of Conventional Controllers

7．2．1 Fuzzy Tuning of PID Controllers

7．2．2 Fuzzy Gain Scheduling

7．2．3 Fuzzy Supervision of Conventional Controllers

7．3 Supervision of Fuzzy Controllers

7．3．1 Rule-Base Supervision

7．3．2 Case Study: Vibration Damping for a Flexible Robot

7．3．3 Supervised Fuzzy Learning Control

7．3．4 Case Study: Fault-Tolerant Aircraft Control

7．4 Summary

7．5 For Further Study

7．6 Design Problems

CHAPTER 8 Perspectives on Fuzzy Control

8．1 Overview

8．2 Fuzzy Versus Conventional Control

8．2．1 Modeling Issues and Design Methodology

8．2．2 Stability and Performance Analysis

8．2．3 Implementation and General Issues

8．3 Neural Networks

8．3．1 Multilayer Perceptrons

8．3．2 Radial Basis Function Neural Networks

8．3．3 Relationships Between Fuzzy Systems and Neural Networks

8．4 Genetic Algorithms

8．4．1 Genetic Algorithms: A Tutorial

8．4．2 Genetic Algorithms for Fuzzy System Design and Tuning

8．5 Knowledge-Based Systems

8．5．1 Expert Control

8．5．2 Planning Systems for Control

8．6 Intelligent and Autonomous Control

8．6．1 What Is Intelligent Control ?

8．6．2 Architecture and Characteristics

8．6．3 Autonomy

8．6．4 Example: Intelligent Vehicle and Highway Systems

8．7 Summary

8．8 For Further Study

8．9 Exercises

BIBLIOGRAPHY

INDEX