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Title:
Fuzzy controller design : theory and applications
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Series:
Control engineering ; 19
Publication Information:
Boca Raton, FL : CRC /Taylor & Francis, 2006
ISBN:
9780849337475
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30000010102024 TJ213 K68 2006 Open Access Book Book
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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

Prefacep. v
Authorsp. xi
Chapter 1 Introductionp. 1
Referencesp. 5
Chapter 2 Fuzzy Controller Designp. 9
2.1 Fuzzy Setsp. 9
2.2 Linguistic Variablesp. 14
2.3 Fuzzy Rulesp. 18
2.3.1 Fuzzy Implicationp. 23
2.3.2 Defuzzificationp. 29
2.4 Fuzzy Controller Structurep. 34
2.4.1 Fuzzy Rule Tablep. 36
2.4.2 Choice of Shape, Number, and Distribution of Fuzzy Setsp. 39
2.5 Fuzzy Controller Stabilityp. 44
Referencesp. 70
Chapter 3 Initial Setting of Fuzzy Controllersp. 75
3.1 Fuzzy Emulation of P-I-D Control Algorithmsp. 76
3.1.1 Fuzzy Emulation of a PID Controllerp. 77
3.1.1.1 Fuzzy Emulation of a PID Controller - Variant Ap. 79
3.1.1.2 Fuzzy Emulation of a PID Controller - Variant Bp. 87
3.1.1.3 Fuzzy Emulation of a PID Controller - Variant Cp. 89
3.1.1.4 Sugeno Type of Fuzzy PID Controllerp. 90
3.2 Model Reference-Based Initial Setting of Fuzzy Controllersp. 90
3.3 Phase Plane-Based Initial Setting of Fuzzy Controllersp. 95
3.4 Practical Examples: Initial Setting of a Fuzzy Controllerp. 98
3.4.1 Emulation of a PI Controllerp. 100
3.4.2 Model Reference-Based Initial Settingp. 102
3.4.3 Phase Plane-Based Initial Settingp. 106
Referencesp. 107
Chapter 4 Complex Fuzzy Controller Structuresp. 109
4.1 Hybrid Fuzzy Controlp. 110
4.2 Adaptive Fuzzy Controlp. 119
4.2.1 Direct and Indirect Adaptive Controlp. 122
4.2.2 Model Reference Fuzzy Adaptive Control Systemsp. 126
4.2.2.1 Sensitivity Model-Based Adaptationp. 129
4.2.2.2 Integral Criterion-Based Adaptationp. 139
4.2.2.3 Model Reference Adaptive Control with Fuzzy Adaptationp. 145
4.2.3 Multiple Fuzzy Rule Table-Based Adaptationp. 165
4.2.4 Fuzzy MRAC Contact Force Controlp. 167
4.2.5 Fuzzy MRAC Angular Speed Controlp. 182
Referencesp. 192
Chapter 5 Self-Organizing Fuzzy Controllersp. 197
5.1 Self-Organizing Fuzzy Control Based on the Direct Lyapunov Methodp. 199
5.2 Self-Organizing Fuzzy Control Based on the Hurwitz Stability Criteriap. 212
5.3 Self-Organizing Fuzzy Control Based on Sensitivity Functionsp. 235
5.3.1 Basic Concept of System Sensitivityp. 236
5.3.2 Synthesis of a Self-Organizing Fuzzy Algorithmp. 239
5.3.3 Example: Multiple Fuzzy Rule Table-Based Controlp. 286
5.3.4 Self-Organizing Fuzzy Control with a Self-Learning Integral Termp. 291
Referencesp. 297
Chapter 6 Fuzzy Controllers as Matlab Superblocksp. 301
6.1 Features of Matlab Fuzzy Logic Toolboxp. 301
6.1.1 FIS Editorp. 301
6.1.2 Membership Function Editorp. 302
6.1.3 Rule Editorp. 303
6.1.4 Rule Viewerp. 303
6.1.5 Defuzzification Methods in FLTp. 305
6.1.6 FLT Commandsp. 306
6.2 Hybrid Fuzzy Controller Super-Block for Matlabp. 306
6.3 Polynomial-Based PSLFLC Matlab Super-Blockp. 309
6.4 Sensitivity Model-Based SLFLC Matlab Super-Blockp. 317
6.5 Design Project: Fuzzy Control of a Electro-Hydraulic Servo Systemp. 326
6.5.1 Mathematical Model of a Control Processp. 326
6.5.2 Simulation Modelp. 328
6.5.3 Fuzzy Controller Design Specificationsp. 329
Referencesp. 334
Chapter 7 Implementation of Fuzzy Controllers for Industrial Applicationsp. 335
7.1 Brief Overview of Industrial Fuzzy Controllersp. 335
7.2 Implementation Platforms for Industrial Fuzzy Logic Controllersp. 338
7.2.1 Microcomputer-Based Fuzzy Controller Implementationp. 339
7.2.2 PLC-Based Fuzzy Gain Scheduling Control of Condensate Levelp. 343
7.2.2.1 The Condenser Modelp. 344
7.2.2.2 Standard Condensate Level Controlp. 345
7.2.2.3 Fuzzy Gain Scheduling Condensate Level Controlp. 347
7.2.2.4 PLC Siemens Simatic S7-216 Step 7 Program of FGS Condensate Level Controlp. 354
7.2.3 PLC-Based Self-Learning Fuzzy Controller Implementationp. 354
7.2.3.1 PPSOFC - Self-Organizing Fuzzy Controller Function Blockp. 359
7.3 Examples of Fuzzy Controller Applications in Process Controlp. 367
7.3.1 PC-Based Fuzzy-Predictive Control of a Road Tunnel Ventilation Systemp. 367
7.3.1.1 The Structure of a Fuzzy-Predictive Controllerp. 367
7.3.1.2 Air Flow Predictionp. 368
7.3.1.3 Prediction of Number of Jet-Fansp. 369
7.3.1.4 Tunnel Parameters Identificationp. 371
7.3.1.5 Fuzzy Controllerp. 373
7.3.1.6 Simulation Experimentsp. 375
7.3.1.7 FBD-Based Implementation of a Fuzzy-Predictive Controllerp. 380
7.3.2 Fuzzy Control of Anesthesiap. 381
Referencesp. 388
Indexp. 393
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