Description
Book SynopsisThis book addresses some of the challenges suffered by the well-known and robust sliding-mode control paradigm. The authors show how the fusion of fuzzy systems with sliding-mode controllers can alleviate some of these problems and promote applicability.
Fuzzy systems used as soft switches eliminate high-frequency signal oscillations and can substantially lower the noise sensitivity of sliding-mode controllers. The amount of a priori knowledge required concerning the nominal structure and parameters of a nonlinear system is also shown to be much reduced by exploiting the general function-approximation property of fuzzy systems so as to use them as identifiers.
The main features of this book include:
• a review of various existing structures of sliding-mode fuzzy control;
• a guide to the fundamental mathematics of sliding-mode fuzzy controllers and their stability analysis;
• state-of-the-art procedures for the design of a sliding-mode fuzzy controller;
• source codes including MATLAB® and Simulink® codes illustrating the simulation of these controllers, particularly the adaptive controllers;
• a short bibliography for each chapter for readers interested in learning more on a particular subject; and
• illustrative examples and simulation results to support the main claims made in the text.
Academic researchers and graduate students interested in the control of nonlinear systems and particularly those working in sliding-mode controller design will find this book a valuable source of comparative information on existing controllers and ideas for the development of new ones.
Table of ContentsMathematica Preliminaries.- Fuzzy and Fuzzy Neural-Network Systems: Type 1 and Type 2.- Sliding-Mode Control: Design, Advantages and Challenges.- Sliding-Mode Fuzzy-Logic Controllers.- Sliding-Mode Fuzzy Neural-Network Controllers.- Sliding-Mode Fuzzy Neural-Network Controllers to Control Systems over Networks.- Simulation Results.
