Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

Jinkun Liu

ISBN 10: 3642348157 / ISBN 13: 9783642348150

Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Jan 2013, 2013

About this Item

Neuware -Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.This book is intended for the researchers in the fields ofneural adaptivecontrol, mechanical systems, Matlab simulation, engineering design, robotics and automation.Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 384 pp. Englisch.

Seller Inventory # 9783642348150

Bibliographic Details

• Title: Radial Basis Function (RBF) Neural Network Control for Mechanical Systems
• Publisher: Springer Berlin Heidelberg, Springer Berlin Heidelberg Jan 2013
• Publication Date: 2013
• Language: English
• ISBN 10: 3642348157
• ISBN 13: 9783642348150
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 26D millimeters 241 millimeters width by 160 millimeters height by 26 millimeters depth
• Weight: 740 grams

About this title

Synopsis

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.
 
This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation.

Jinkun Liu is a professor at Beijing University of Aeronautics and Astronautics.