Fuzzy Controller Design: Theory and Applications (Automation and Control Engineering)

✍ Scribed by Zdenko Kovacic, Stjepan Bogdan

• Publisher: CRC Press
• Year: 2005
• Tongue: English
• Leaves: 404
• Category: Library

✦ Synopsis

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

7.1 BRIEF OVERVIEW OF INDUSTRIAL FUZZY CONTROLLERS......Page 1
7.2.1 Microcomputer-Based Fuzzy Controller Implementation......Page 5
About the Organization of the Book......Page 7
7.2.2.2 Standard Condensate Level Control......Page 11
Contents......Page 12
3.1.1.3 Fuzzy Emulation of a PID Controller — Variant C......Page 15
Table of Contents......Page 22
6.2 HYBRID FUZZY CONTROLLER SUPER-BLOCK FOR MATLAB......Page 6
7.2.2.1 The Condenser Model......Page 10
4.2.2.1 Sensitivity Model-Based Adaptation......Page 21
6.5.1 Mathematical Model of a Control Process......Page 26
6.5.2 Simulation Model......Page 28
4.2.2.2 Integral Criterion-Based Adaptation......Page 31
2.5 FUZZY CONTROLLER STABILITY......Page 36
REFERENCES......Page 62
Table of Contents......Page 87
6.1.2 Membership Function Editor......Page 2
6.1.4 Rule Viewer......Page 3
7.2.2.3 Fuzzy Gain Scheduling Condensate Level Control......Page 13
5.2 SELF-ORGANIZING FUZZY CONTROL BASED ON THE HURWITZ STABILITY CRITERIA......Page 16
3.4 PRACTICAL EXAMPLES: INITIAL SETTING OF A FUZZY CONTROLLER......Page 24
3.4.3 Phase Plane-Based Initial Setting......Page 32
7.3.1.1 The Structure of a Fuzzy-Predictive Controller......Page 33
Table of Contents......Page 121
4.2.1 Direct and Indirect Adaptive Control......Page 14
4.2.2 Model Reference Fuzzy Adaptive Control Systems......Page 18
7.3.1.4 Tunnel Parameters Identification......Page 37
4.2.3 Multiple Fuzzy Rule Table-Based Adaptation......Page 57
4.2.4 Fuzzy MRAC Contact Force Control......Page 59
4.2.5 Fuzzy MRAC Angular Speed Control......Page 74
REFERENCES......Page 84
Table of Contents......Page 209
7.3.1.5 Fuzzy Controller......Page 39
5.3.1 Basic Concept of System Sensitivity......Page 40
5.3.2 Synthesis of a Self-Organizing Fuzzy Algorithm......Page 43
5.3.3 Example: Multiple Fuzzy Rule Table-Based Control......Page 90
5.3.4 Self-Organizing Fuzzy Control with a Self-Learning Integral Term......Page 95
REFERENCES......Page 101
Table of Contents......Page 313
7.2.2 PLC-Based Fuzzy Gain Scheduling Control of Condensate Level......Page 9
6.4 SENSITIVITY MODEL-BASED SLFLC MATLAB SUPER-BLOCK......Page 17
6.5.3 Fuzzy Controller Design Specifications......Page 29
7.3.1.2 Air Flow Prediction......Page 34
Table of Contents......Page 347
7.2 IMPLEMENTATION PLATFORMS FOR INDUSTRIAL FUZZY LOGIC CONTROLLERS......Page 4
7.2.3 PLC-Based Self-Learning Fuzzy Controller Implementation......Page 20
7.2.3.1 PPSOFC — Self-Organizing Fuzzy Controller Function Block......Page 25
7.3.1.3 Prediction of Number of Jet-Fans......Page 35
7.3.1.6 Simulation Experiments......Page 41
7.3.1.7 FBD-Based Implementation of a Fuzzy-Predictive Controller......Page 46
7.3.2 Fuzzy Control of Anesthesia......Page 47
REFERENCES......Page 54