9780849337475

Fuzzy Controller Design: Theory and Applications

by ;
  • ISBN13:

    9780849337475

  • ISBN10:

    084933747X

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2005-12-12
  • Publisher: CRC Press

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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 MATLABŪ/SIMULINKŪ, 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 v
Authors xi
Introduction
1(8)
References
5(4)
Fuzzy Controller Design
9(66)
Fuzzy Sets
9(5)
Linguistic Variables
14(4)
Fuzzy Rules
18(16)
Fuzzy Implication
23(6)
Defuzzification
29(5)
Fuzzy Controller Structure
34(10)
Fuzzy Rule Table
36(3)
Choice of Shape, Number, and Distribution of Fuzzy Sets
39(5)
Fuzzy Controller Stability
44(31)
References
70(5)
Initial Setting of Fuzzy Controllers
75(34)
Fuzzy Emulation of P-I-D Control Algorithms
76(14)
Fuzzy Emulation of a PID Controller
77(2)
Fuzzy Emulation of a PID Controller --- Variant A
79(8)
Fuzzy Emulation of a PID Controller --- Variant B
87(2)
Fuzzy Emulation of a PID Controller --- Variant C
89(1)
Sugeno Type of Fuzzy PID Controller
90(1)
Model Reference-Based Initial Setting of Fuzzy Controllers
90(5)
Phase Plane-Based Initial Setting of Fuzzy Controllers
95(3)
Practical Examples: Initial Setting of a Fuzzy Controller
98(11)
Emulation of a PI Controller
100(2)
Model Reference-Based Initial Setting
102(4)
Phase Plane-Based Initial Setting
106(1)
References
107(2)
Complex Fuzzy Controller Structures
109(88)
Hybrid Fuzzy Control
110(9)
Adaptive Fuzzy Control
119(78)
Direct and Indirect Adaptive Control
122(4)
Model Reference Fuzzy Adaptive Control Systems
126(3)
Sensitivity Model-Based Adaptation
129(10)
Integral Criterion-Based Adaptation
139(6)
Model Reference Adaptive Control with Fuzzy Adaptation
145(20)
Multiple Fuzzy Rule Table-Based Adaptation
165(2)
Fuzzy MRAC Contact Force Control
167(15)
Fuzzy MRAC Angular Speed Control
182(10)
References
192(5)
Self-Organizing Fuzzy Controllers
197(104)
Self-Organizing Fuzzy Control Based on the Direct Lyapunov Method
199(13)
Self-Organizing Fuzzy Control Based on the Hurwitz Stability Criteria
212(23)
Self-Organizing Fuzzy Control Based on Sensitivity Functions
235(66)
Basic Concept of System Sensitivity
236(3)
Synthesis of a Self-Organizing Fuzzy Algorithm
239(47)
Example: Multiple Fuzzy Rule Table-Based Control
286(5)
Self-Organizing Fuzzy Control with a Self-Learning Integral Term
291(6)
References
297(4)
Fuzzy Controllers as Matlab® Superblocks
301(34)
Features of Matlab Fuzzy Logic Toolbox
301(5)
FIS Editor
301(1)
Membership Function Editor
302(1)
Rule Editor
303(1)
Rule Viewer
303(2)
Defuzzification Methods in FLT
305(1)
FLT Commands
306(1)
Hybrid Fuzzy Controller Super-Block for Matlab
306(3)
Polynomial-Based PSLFLC Matlab Super-Block
309(8)
Sensitivity Model-Based SLFLC Matlab Super-Block
317(9)
Design Project: Fuzzy Control of a Electro-Hydraulic Servo System
326(9)
Mathematical Model of a Control Process
326(2)
Simulation Model
328(1)
Fuzzy Controller Design Specifications
329(5)
References
334(1)
Implementation of Fuzzy Controllers for Industrial Applications
335(58)
Brief Overview of Industrial Fuzzy Controllers
335(3)
Implementation Platforms for Industrial Fuzzy Logic Controllers
338(29)
Microcomputer-Based Fuzzy Controller Implementation
339(4)
PLC-Based Fuzzy Gain Scheduling Control of Condensate Level
343(1)
The Condenser Model
344(1)
Standard Condensate Level Control
345(2)
Fuzzy Gain Scheduling Condensate Level Control
347(7)
PLC Siemens Simatic S7-216 Step 7 Program of FGS Condensate Level Control
354(1)
PLC-Based Self-Learning Fuzzy Controller Implementation
354(5)
PPSOFC --- Self-Organizing Fuzzy Controller Function Block
359(8)
Examples of Fuzzy Controller Applications in Process Control
367(26)
PC-Based Fuzzy-Predictive Control of a Road Tunnel Ventilation System
367(1)
The Structure of a Fuzzy-Predictive Controller
367(1)
Air Flow Prediction
368(1)
Prediction of Number of Jet-Fans
369(2)
Tunnel Parameters Identification
371(2)
Fuzzy Controller
373(2)
Simulation Experiments
375(5)
FBD-Based Implementation of a Fuzzy-Predictive Controller
380(1)
Fuzzy Control of Anesthesia
381(7)
References
388(5)
Index 393

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