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Process Modelling for Control concentrates on the modelling steps underlying a successful control design, answering questions like: How should I carry out the identification of my process to obtain a good model?How can I assess the quality of a model before to using it in control design?How can I ensure that a controller will stabilise a real process well enough before implementation?What is the most efficient method of order reduction to simplify the implementation of high-order controllers? System identification, model/controller validation and order reduction are studied in a common framework. Detailed worked examples, representative of various industrial applications, are given. This monograph uses mathematics convenient to researchers interested in real applications and to practising engineers interested in control theory. It enables control engineers to improve their methods and provides academics and graduate students with an all-round view of recent results in modelling for control.
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Advances in Industrial Control
Other titles published in this Series: Nonlinear Identification and Control Guoping Liu Digital Controller Implementation and Fragility Robert S.H. Istepanian and James F. Whidborne (Eds.) Optimisation of Industrial Processes at Supervisory Level Doris Sa´ez, Aldo Cipriano and Andrzej W. Ordys Applied Predictive Control Huang Sunan, Tan Kok Kiong and Lee Tong Heng Hard Disk Drive Servo Systems Ben M. Chen, Tong H. Lee and Venkatakrishnan Venkataramanan Robust Control of Diesel Ship Propulsion Nikolaos Xiros Hydraulic Servo-systems Moheiddine Jelali and Andreas Kroll Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques Silvio Simani, Cesare Fantuzzi and Ron J. Patton Strategies for Feedback Linearisation: A Dynamic Neural Network Approach Freddy Garces, Victor M. Becerra, Chandrasekhar Kambhampati and Kevin Warwick Robust Autonomous Guidance Alberto Isidori, Lorenzo Marconi and Andrea Serrani Dynamic Modelling of Gas Turbines Gennady G. Kulikov and Haydn A. Thompson (Eds.) Control of Fuel Cell Power Systems Jay T. Pukrushpan, Anna G. Stefanopoulou and Huei Peng Fuzzy Logic, Identification and Predictive Control Jairo Espinosa, Joos Vanderwalle and Vincent Wertz Optimal Real-time Control of Sewer Networks Magdalene Marinaki and Markos Papageorgiou Computational Intelligence in Time Series Forecasting Ajoy K. Palit and Dobrivoje Popovic Modelling and Control of mini-Flying Machines Pedro Castillo, Rogelio Lozano and Alejandro Dzul Rudder and Fin Ship Roll Stabilization Tristan Perez Control of Passenger Traffic Systems in Buildings Sandor Markon Publication due January 2006 Nonlinear H2 /H∞ Feedback control Murad Abu-Khalaf, Frank L. Lewis and Jie Juang Publication due January 2006
Benoıˆt Codrons
Process Modelling for Control A Unified Framework Using Standard Black-box Techniques
With 74 Figures
Benoıˆt Codrons, Dr.Eng. Laborelec, 125 Rue de Rhode, B-1630 Linkebeek, Belgium
British Library Cataloguing in Publication Data Codrons, Benoıˆt Process modelling for control : a unified framework using standard black-box techniques. — (Advances in industrial control) 1. Process control — Mathematical models I. Title 629.8′312 ISBN 1852339187 Library of Congress Control Number 2005923269 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Advances in Industrial Con