Modelling and Control for Intelligent Industrial Systems Adaptive Al
Incorporating intelligence in industrial systems can help to increase productivity, cut-off production costs, and to improve working conditions and safety in industrial environments. This need has resulted in the rapid development of modeling and control
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Intelligent Systems Reference Library, Volume 7 Editors-in-Chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: [email protected]
Prof. Lakhmi C. Jain University of South Australia Adelaide Mawson Lakes Campus South Australia 5095 Australia E-mail: [email protected]
Further volumes of this series can be found on our homepage: springer.com Vol. 1. Christine L. Mumford and Lakhmi C. Jain (Eds.) Computational Intelligence: Collaboration, Fusion and Emergence, 2009 ISBN 978-3-642-01798-8 Vol. 2. Yuehui Chen and Ajith Abraham Tree-Structure Based Hybrid Computational Intelligence, 2009 ISBN 978-3-642-04738-1 Vol. 3. Anthony Finn and Steve Scheding Developments and Challenges for Autonomous Unmanned Vehicles, 2010 ISBN 978-3-642-10703-0 Vol. 4. Lakhmi C. Jain and Chee Peng Lim (Eds.) Handbook on Decision Making: Techniques and Applications, 2010 ISBN 978-3-642-13638-2 Vol. 5. George A. Anastassiou Intelligent Mathematics: Computational Analysis, 2010 ISBN 978-3-642-17097-3 Vol. 6. Ludmila Dymowa Soft Computing in Economics and Finance, 2011 ISBN 978-3-642-17718-7 Vol. 7. Gerasimos G. Rigatos Modelling and Control for Intelligent Industrial Systems, 2011 ISBN 978-3-642-17874-0
Gerasimos G. Rigatos
Modelling and Control for Intelligent Industrial Systems Adaptive Algorithms in Robotics and Industrial Engineering
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Gerasimos G. Rigatos Unit of Industrial Automation, Industrial Systems Institute, Rion Patras, Greece 26504 E-mail: [email protected]
ISBN 978-3-642-17874-0
e-ISBN 978-3-642-17875-7
DOI 10.1007/978-3-642-17875-7 Intelligent Systems Reference Library
ISSN 1868-4394
c 2011 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, 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. Typeset & Cover Design: Scientific Publishing Services Pvt. Ltd., Chennai, India. Printed on acid-free paper 987654321 springer.com
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Foreword
There are two main requirements for the development of intelligent industrial systems: (i) learning and adaptation in unknown environments, (ii) compensation of model uncertainties as well as of unknown or stochastic external disturbances. Learning can be performed with the use of gradient-type algorithms (also applied t
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