Radial Basis Function (RBF) Neural Network Control for Mechanical Systems
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 bo
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Jinkun Liu
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems Design, Analysis and Matlab Simulation
With 170 figures
Jinkun Liu Department of Intelligent System and Control Engineering School of Automation Science and Electrical Engineering Beihang University Beijing, China, People’s Republic
TUP ISBN 978-7-302-30255-1 ISBN 978-3-642-34815-0 ISBN 978-3-642-34816-7 (eBook) DOI 10.1007/978-3-642-34816-7 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2012955859 # Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Recent years have seen a rapid development of neural network control techniques and their successful applications. Numerous theoretical studies and actual industrial implementations demonstrate that artificial neural network is a good candidate for function approximation and control system design in solving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control, and fuzzy systems, have been published in various books, journals, and co
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