Neural Network-Based State Estimation of Nonlinear Systems Applicati
This series aims to report new developments in the fields of control and information sciences –quickly, informally and at a high level. The type material considered for publication includes: 1. Preliminary drafts of monographs and advanced textbooks 2. Le
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Heidar A. Talebi, Farzaneh Abdollahi, Rajni V. Patel, Khashayar Khorasani
Neural Network-Based State Estimation of Nonlinear Systems Application to Fault Detection and Isolation
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Series Advisory Board P. Fleming, P. Kokotovic, A.B. Kurzhanski, H. Kwakernaak, A. Rantzer, J.N. Tsitsiklis Authors Heidar A. Talebi Department of Electrical Engineering Amirkabir University of Technology 424 Hafez Ave. 15914 Tehran Iran [email protected] Rajni V. Patel Department of Electrical & Computer Engineering University of Western Ontario 1151 Richmond Street North London ON N6A 5B9 Canada [email protected]
Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology 424 Hafez Ave. 15914 Tehran Iran f [email protected] Khashayar Khorasani Department of Electrical & Computer Engineering Concordia University 1455 Maisonneuve Blvd. West, EV005.126 Montreal QC H3G 1M8 Canada [email protected]
ISSN 0170-8643 e-ISSN 1610-7411 ISBN 978-1-4419-1437-8 e-ISBN 978-1-4419-1438-5 DOI 10.1007/978-1-4419-1438-5 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009940450 c Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
To My Family (H. A. T.) My Parents (F. A.) My Family (R. V. P.) My Family (K. K.)
Preface
The state of a process specifies its behavior, and many control schemes such as inverse dynamics and feedback linearization rely on the availability of all the system states. However, in many practical systems only the input and output of a system are measurable. Therefore, estimating the states of a system plays a crucial role in monitoring the process, detecting and diagnosing of faults, and achieving better performance. Furthermore, most practical systems are nonlinear, and using linearization or quasi-linearization methods limits the estimation accuracy to a small dynamic range. Several conventional nonlinear observers have been proposed during the past couple of decades. However, most of this work relies on exact a priori knowledge of the system nonlinearities. This assumption is rarely satisfied for most practical processes where determining an exact model is quite a difficult, if not impossible, task. Robot manipulators with flexible joints or link
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