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Searching... | 30000010102024 | TJ213 K68 2006 | Open Access Book | Book | Searching... |
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Summary
Summary
Fuzzy control methods are critical for meeting the demands of complex nonlinear systems. They bestow robust, adaptive, and self-correcting character to complex systems that demand high stability and functionality beyond the capabilities of traditional methods. A thorough treatise on the theory of fuzzy logic control is out of place on the design bench. That is why Fuzzy Controller Design: Theory and Applications offers laboratory- and industry-tested algorithms, techniques, and formulations of real-world problems for immediate implementation.
With surgical precision, the authors carefully select the fundamental elements of fuzzy logic control theory necessary to formulate effective and efficient designs. The book supplies a springboard of knowledge, punctuated with examples worked out in MATLAB®/SIMULINK®, from which newcomers to the field can dive directly into applications. It systematically covers the design of hybrid, adaptive, and self-learning fuzzy control structures along with strategies for fuzzy controller design suitable for on-line and off-line operation. Examples occupy an entire chapter, with a section devoted to the simulation of an electro-hydraulic servo system. The final chapter explores industrial applications with emphasis on techniques for fuzzy controller implementation and different implementation platforms for various applications.
With proven methods based on more than a decade of experience, Fuzzy Controller Design: Theory and Applications is a concise guide to the methodology, design steps, and formulations for effective control solutions.
Table of Contents
Preface | p. v |
Authors | p. xi |
Chapter 1 Introduction | p. 1 |
References | p. 5 |
Chapter 2 Fuzzy Controller Design | p. 9 |
2.1 Fuzzy Sets | p. 9 |
2.2 Linguistic Variables | p. 14 |
2.3 Fuzzy Rules | p. 18 |
2.3.1 Fuzzy Implication | p. 23 |
2.3.2 Defuzzification | p. 29 |
2.4 Fuzzy Controller Structure | p. 34 |
2.4.1 Fuzzy Rule Table | p. 36 |
2.4.2 Choice of Shape, Number, and Distribution of Fuzzy Sets | p. 39 |
2.5 Fuzzy Controller Stability | p. 44 |
References | p. 70 |
Chapter 3 Initial Setting of Fuzzy Controllers | p. 75 |
3.1 Fuzzy Emulation of P-I-D Control Algorithms | p. 76 |
3.1.1 Fuzzy Emulation of a PID Controller | p. 77 |
3.1.1.1 Fuzzy Emulation of a PID Controller - Variant A | p. 79 |
3.1.1.2 Fuzzy Emulation of a PID Controller - Variant B | p. 87 |
3.1.1.3 Fuzzy Emulation of a PID Controller - Variant C | p. 89 |
3.1.1.4 Sugeno Type of Fuzzy PID Controller | p. 90 |
3.2 Model Reference-Based Initial Setting of Fuzzy Controllers | p. 90 |
3.3 Phase Plane-Based Initial Setting of Fuzzy Controllers | p. 95 |
3.4 Practical Examples: Initial Setting of a Fuzzy Controller | p. 98 |
3.4.1 Emulation of a PI Controller | p. 100 |
3.4.2 Model Reference-Based Initial Setting | p. 102 |
3.4.3 Phase Plane-Based Initial Setting | p. 106 |
References | p. 107 |
Chapter 4 Complex Fuzzy Controller Structures | p. 109 |
4.1 Hybrid Fuzzy Control | p. 110 |
4.2 Adaptive Fuzzy Control | p. 119 |
4.2.1 Direct and Indirect Adaptive Control | p. 122 |
4.2.2 Model Reference Fuzzy Adaptive Control Systems | p. 126 |
4.2.2.1 Sensitivity Model-Based Adaptation | p. 129 |
4.2.2.2 Integral Criterion-Based Adaptation | p. 139 |
4.2.2.3 Model Reference Adaptive Control with Fuzzy Adaptation | p. 145 |
4.2.3 Multiple Fuzzy Rule Table-Based Adaptation | p. 165 |
4.2.4 Fuzzy MRAC Contact Force Control | p. 167 |
4.2.5 Fuzzy MRAC Angular Speed Control | p. 182 |
References | p. 192 |
Chapter 5 Self-Organizing Fuzzy Controllers | p. 197 |
5.1 Self-Organizing Fuzzy Control Based on the Direct Lyapunov Method | p. 199 |
5.2 Self-Organizing Fuzzy Control Based on the Hurwitz Stability Criteria | p. 212 |
5.3 Self-Organizing Fuzzy Control Based on Sensitivity Functions | p. 235 |
5.3.1 Basic Concept of System Sensitivity | p. 236 |
5.3.2 Synthesis of a Self-Organizing Fuzzy Algorithm | p. 239 |
5.3.3 Example: Multiple Fuzzy Rule Table-Based Control | p. 286 |
5.3.4 Self-Organizing Fuzzy Control with a Self-Learning Integral Term | p. 291 |
References | p. 297 |
Chapter 6 Fuzzy Controllers as Matlab Superblocks | p. 301 |
6.1 Features of Matlab Fuzzy Logic Toolbox | p. 301 |
6.1.1 FIS Editor | p. 301 |
6.1.2 Membership Function Editor | p. 302 |
6.1.3 Rule Editor | p. 303 |
6.1.4 Rule Viewer | p. 303 |
6.1.5 Defuzzification Methods in FLT | p. 305 |
6.1.6 FLT Commands | p. 306 |
6.2 Hybrid Fuzzy Controller Super-Block for Matlab | p. 306 |
6.3 Polynomial-Based PSLFLC Matlab Super-Block | p. 309 |
6.4 Sensitivity Model-Based SLFLC Matlab Super-Block | p. 317 |
6.5 Design Project: Fuzzy Control of a Electro-Hydraulic Servo System | p. 326 |
6.5.1 Mathematical Model of a Control Process | p. 326 |
6.5.2 Simulation Model | p. 328 |
6.5.3 Fuzzy Controller Design Specifications | p. 329 |
References | p. 334 |
Chapter 7 Implementation of Fuzzy Controllers for Industrial Applications | p. 335 |
7.1 Brief Overview of Industrial Fuzzy Controllers | p. 335 |
7.2 Implementation Platforms for Industrial Fuzzy Logic Controllers | p. 338 |
7.2.1 Microcomputer-Based Fuzzy Controller Implementation | p. 339 |
7.2.2 PLC-Based Fuzzy Gain Scheduling Control of Condensate Level | p. 343 |
7.2.2.1 The Condenser Model | p. 344 |
7.2.2.2 Standard Condensate Level Control | p. 345 |
7.2.2.3 Fuzzy Gain Scheduling Condensate Level Control | p. 347 |
7.2.2.4 PLC Siemens Simatic S7-216 Step 7 Program of FGS Condensate Level Control | p. 354 |
7.2.3 PLC-Based Self-Learning Fuzzy Controller Implementation | p. 354 |
7.2.3.1 PPSOFC - Self-Organizing Fuzzy Controller Function Block | p. 359 |
7.3 Examples of Fuzzy Controller Applications in Process Control | p. 367 |
7.3.1 PC-Based Fuzzy-Predictive Control of a Road Tunnel Ventilation System | p. 367 |
7.3.1.1 The Structure of a Fuzzy-Predictive Controller | p. 367 |
7.3.1.2 Air Flow Prediction | p. 368 |
7.3.1.3 Prediction of Number of Jet-Fans | p. 369 |
7.3.1.4 Tunnel Parameters Identification | p. 371 |
7.3.1.5 Fuzzy Controller | p. 373 |
7.3.1.6 Simulation Experiments | p. 375 |
7.3.1.7 FBD-Based Implementation of a Fuzzy-Predictive Controller | p. 380 |
7.3.2 Fuzzy Control of Anesthesia | p. 381 |
References | p. 388 |
Index | p. 393 |