Fuzzy Controller Design offers laboratory and industry tested algorithms, techniques, and formulations of real-world problems for immediate implementation.

Features:

• Presents clear, practical, easy-to-use methods for designing and implementing fuzzy control systems
• Addresses the heuristic nature of fuzzy controller design and methods to overcome the resulting problems
• Examines the design of hybrid, adaptive, and self-learning fuzzy control structures
• Explains original concepts and methods such as phase-plane-based initial presetting, sensitivity model-based self-organization, and PLC-based implementation
• Provides easy-to-follow worked examples in MATLAB

Contents

Fuzzy Controller Design

• Fuzzy Sets
• Linguistic Variables
• Fuzzy Rules
• Fuzzy Controller Structure
• Fuzzy Controller Stability

Initial Setting of Fuzzy Controllers

• Fuzzy Emulation of P-I-D Control Algorithms
• Model Reference Based Initial Setting of Fuzzy Controllers
• Phase Plane-Based Initial Setting of Fuzzy Controllers
• Practical Examples: Initial Setting of a Fuzzy Controller

Complex Fuzzy Controller Structures

• Hybrid Fuzzy Control
• Adaptive Fuzzy Control

Self-Organizing Fuzzy Controller

• Self-Organizing Fuzzy Control Based on the Direct Lyapunov Method
• Self-Organizing Fuzzy Control Based on the Hurwitz Stability Criteria
• Self-Organizing Fuzzy Control Based on Sensitivity Functions

Fuzzy Controllers as Matlab Superblocks

• Features of MATLAB Fuzzy Logic Toolbox
• Hybrid Fuzzy Controller Super-Block for Matlab
• Polynomial-Based PSLFLC Matlab Super-Block
• Sensitivity Model-Based SLFLC MATLAB Super-Block
• Design Project: Fuzzy Control of an Electro-Hydraulic Servo System

Implementation of Fuzzy Controllers for Industrial Applications

• Brief Overview of Industrial Fuzzy Controllers
• Implementation Platforms for Industrial Fuzzy Logic Controllers
• Examples of Fuzzy Controller Applications in Process Control

Index

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