Fuzzy and Neural Adaptive Control of a Class of Nonlinear Systems

Mohamed Bahita

ISBN 10: 3848489201 / ISBN 13: 9783848489206

Published by LAP LAMBERT Academic Publishing Apr 2012, 2012

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -In this book, two types of direct adaptive control schemes for a class of nonlinear systems are proposed. Based on the feedback linearization theory, the architecture employs for the first approach the fuzzy logic reasoning of Takagi Sugeno (TS) type and uses for the second approach the strategy of neural network reasoning of radial basis function (RBF) type to approximate the feedback linearization control law. In each case, the parameters of the adaptive controller are adapted according to a law derived using Lyapunov stability theory. The adaptive controller is applied in simulation to control three nonlinear systems in both the fuzzy and the neural network methods. 64 pp. Englisch.

Seller Inventory # 9783848489206

Bibliographic Details

• Title: Fuzzy and Neural Adaptive Control of a Class of Nonlinear Systems
• Publisher: LAP LAMBERT Academic Publishing Apr 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3848489201
• ISBN 13: 9783848489206
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 113 grams

About this title

Synopsis

In this book, two types of direct adaptive control schemes for a class of nonlinear systems are proposed. Based on the feedback linearization theory, the architecture employs for the first approach the fuzzy logic reasoning of Takagi Sugeno (TS) type and uses for the second approach the strategy of neural network reasoning of radial basis function (RBF) type to approximate the feedback linearization control law. In each case, the parameters of the adaptive controller are adapted according to a law derived using Lyapunov stability theory. The adaptive controller is applied in simulation to control three nonlinear systems in both the fuzzy and the neural network methods.

About the Author

Mohamed Bahita obtained his “Ingénieur“, his Master Degree and his Doctor “ès Sciences” Degree all in control Engineering from the University of Constantine, Algeria. He is currently with the University of BOUMERDES, Algeria. His main interests are in artificial intelligence (fuzzy and neural) and in adaptive control of nonlinear systems.