Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach (Automation and Control Engineering)

✍ Scribed by Gang Feng

• Publisher: CRC Press
• Year: 2010
• Tongue: English
• Leaves: 295
• Series: Automation and Control Engineering Series
• Edition: 1
• Category: Library

✦ Synopsis

Fuzzy logic control (FLC) has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. However, because it is fundamentally model free, conventional FLC suffers from a lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have been developed, with the fuzzy dynamic model or the Takagi and Sugeno (T–S) fuzzy model-based approaches receiving the greatest attention. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach offers a unique reference devoted to the systematic analysis and synthesis of model-based fuzzy control systems. After giving a brief review of the varieties of FLC, including the T–S fuzzy model-based control, it fully explains the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy systems. This enables the book to be self-contained and provides a basis for later chapters, which cover: T–S fuzzy modeling and identification via nonlinear models or data Stability analysis of T–S fuzzy systems Stabilization controller synthesis as well as robust H? and observer and output feedback controller synthesis Robust controller synthesis of uncertain T–S fuzzy systems Time-delay T–S fuzzy systems Fuzzy model predictive control Robust fuzzy filtering Adaptive control of T–S fuzzy systems A reference for scientists and engineers in systems and control, the book also serves the needs of graduate students exploring fuzzy logic control. It readily demonstrates that conventional control technology and fuzzy logic control can be elegantly combined and further developed so that disadvantages of conventional FLC can be avoided and the horizon of conventional control technology greatly extended. Many chapters feature application simulation examples and practical numerical examples based on MATLAB®.

✦ Table of Contents

Analysis and Synthesis of Fuzzy Control Systems......Page 2
Contents......Page 6
Preface......Page 10
Author......Page 14
1.1 Introduction......Page 16
1.2.1 Conventional Fuzzy Control (Mamdani-Type Fuzzy Control)......Page 17
1.2.2 Fuzzy PID Control......Page 19
1.2.3 Neuro–Fuzzy Control or Fuzzy–Neuro Control......Page 20
1.2.4 Fuzzy Sliding Mode Control......Page 21
1.2.5 Adaptive Fuzzy Control......Page 22
1.2.6 Takagi–Sugeno Model-Based Fuzzy Control......Page 23
1.3 Summary......Page 26
2.2 Fuzzy Sets and Related Concepts......Page 28
2.3 Fuzzy Relations and Fuzzy IF–THEN Rules......Page 36
2.4 Fuzzy Reasoning......Page 38
2.5 Fuzzy Models and Fuzzy Systems......Page 41
2.5.2 Takagi–Sugeno Fuzzy Systems......Page 43
2.5.3 Fuzzy Dynamic Systems......Page 44
2.6 Conclusions......Page 46
3.2 T–S Fuzzy Models......Page 48
3.3 Universal Function Approximators......Page 51
3.4 T–S Fuzzy Model Identificationfrom Nonlinear Models......Page 57
3.5.1 Identification of Membership Functions......Page 60
3.5.2 Identification of Local Models......Page 64
3.6 Approximation Error Analysis......Page 65
3.7 Conclusions......Page 66
4.2 Stability Analysis Based on Common Quadratic Lyapunov Functions......Page 68
4.3 Stability Analysis Based on Piecewise Quadratic Lyapunov Functions......Page 73
4.4 Stability Analysis Based on Fuzzy Quadratic Lyapunov Functions......Page 81
4.5 Stability Analysis of T–S Fuzzy Affine Systems Basedon Piecewise Quadratic Lyapunov Functions......Page 84
4.6 Comparis on of Stability Results via Numerical Examples......Page 89
4.7 Conclusions......Page 92
5.2 Stabilization Based on Common Quadratic Lyapunov Functions......Page 94
5.3 Stabilization Based on Piecewise Quadratic Lyapunov Functions......Page 102
5.4 Stabilization Based on Fuzzy Quadratic Lyapunov Functions......Page 108
5.5 Comparison of Stabilization Results via Numerical Examples......Page 112
5.6 Conclusions......Page 116
6.2 Robust H∞ Control Based on Common Quadratic Lyapunov Functions......Page 118
6.3 Robust H∞ Control Based on Piecewise Quadratic Lyapunov Functions......Page 126
6.4 Robust H∞ Control Based on Fuzzy Quadratic Lyapunov Functions......Page 133
6.5 Comparis on of Robust H∞ Control Results via Numerical Examples......Page 136
6.6 Conclusions......Page 138
7.2 Observer and Output Feedback Controller Synthesis Based on Common Quadratic Lyapunov Functions......Page 140
7.3 Observer and Output Feedback Controller Synthesis Based on Piecewise Quadratic Lyapunov Functions......Page 149
7.4 Observer and Output Feedback Controller Synthesis Based on Fuzzy Quadratic Lyapunov Functions......Page 157
7.5 Comparison of Observer Design Results via Numerical Examples......Page 163
7.6 Conclusions......Page 166
8.2 Model of Uncertain T–S Fuzzy Systems......Page 168
8.3 Controller Synthesis Based on Piecewise Quadratic Lyapunov Functions......Page 170
8.3.1 Robust H∞ Performance Analysis......Page 171
8.3.2 Piecewise State Feedback Controller Design......Page 173
8.3.3 Piecewise Output Feedback Controller Design......Page 176
8.4.1 Robust H∞ Performance Analysis......Page 181
8.4.2 State Feedback Controller Design......Page 183
8.4.3 Output Feedback Controller Design......Page 187
8.5 An Example......Page 192
8.6 Conclusions......Page 194
9.2 Model of T–S Fuzzy Systems with Time-Delay......Page 196
9.3 Controller Synthesis Based on Piecewise Quadratic Lyapunov Functionals......Page 198
9.3.1 Delay-Independent H∞ Controller Design......Page 199
9.3.2 Delay-Dependent H∞ Controller Design......Page 201
9.4 Controller Synthesis Based on Fuzzy Quadratic Lyapunov Functionals......Page 206
9.4.1 Delay-Independent H∞ Controller Design......Page 207
9.4.2 Delay-Dependent H∞ Controller Design......Page 209
9.5 An Example......Page 213
9.6 Conclusions......Page 215
10.2 Problem Formulation......Page 216
10.3.1 Fuzzy Min–Max MPC Based on Common Quadratic Lyapunov Functions......Page 220
10.3.2 Fuzzy Min–Max MPC Based on Piecewise Quadratic Lyapunov Functions......Page 223
10.3.3 Constrained Fuzzy MPC......Page 225
10.4 Simulation Examples......Page 228
10.5 Conclusions......Page 236
11.2 Problem Formulation......Page 238
11.3.1 H∞ Filter Design......Page 240
11.3.2 Generalized H2 Filter Design......Page 242
11.4.1 H∞ Filter Design......Page 245
11.4.2 Generalized H2 Filter Design......Page 247
11.5.1 H∞ Filter Design......Page 249
11.5.2 Generalized H2 Filter Design......Page 251
11.6 Simulation Examples......Page 252
11.7 Conclusions......Page 259
12.2 Problem Formulation......Page 260
12.3.1 Adaptation Algorithm......Page 262
12.3.2 Controller Design with Known Parameters......Page 263
12.3.3 Adaptive Control System Design......Page 264
12.3.4 Robust Adaptive Control......Page 267
12.4 A Simulation Example......Page 274
12.5 Conclusions......Page 276
Appendix......Page 278
References......Page 280