Advanced Textbooks in Control and Signal Processing Series Editors Professor Michael J. Grimble, Professor of Industrial Systems and Director Professor Michael A. Johnson, Professor of Control Systems and Deputy Director Industrial Control Centre, Department of Electronic and Electrical Engineering, University of Strathclyde, Graham Hills Building, 50 George Street, Glasgow G1 1QE, UK For further volumes: www.springer.com/series/4045 Karel J. Keesman System Identification An Introduction Karel J. Keesman Systems and Control Group Wageningen University Bornse Weilanden 9 6708 WG, Wageningen Netherlands [email protected] ISSN 1439-2232 ISBN 978-0-85729-521-7 e-ISBN 978-0-85729-522-4 DOI 10.1007/978-0-85729-522-4 Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2011929048 Mathematics Subject Classification: 93E12, 93E24, 93E10, 93E11 © Springer-Verlag London Limited 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as per- mitted 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 publish- ers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of 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 laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: eStudio Calamar S.L. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) To Wil, Esther, Carlijn, and Rick ... Series Editors’ Foreword The topics of control engineering and signal processing continue to flourish and develop. In common with general scientific investigation, new ideas, concepts and interpretations emerge quite spontaneously, and these are then discussed, used, dis- carded or subsumed into the prevailing subject paradigm. Sometimes these innova- tive concepts coalesce into a new sub-discipline within the broad subject tapestry of control and signal processing. This preliminary battle between old and new usually takes place at conferences, through the Internet and in the journals of the discipline. After a little more maturity has been acquired by the new concepts, then archival publication as a scientific or engineering monograph may occur. A new concept in control and signal processing is known to have arrived when sufficient material has evolved for the topic to be taught as a specialised tutorial workshop or as a course to undergraduate, graduate or industrial engineers. Ad- vanced Textbooks in Control and Signal Processing are designed as a vehicle for the systematic presentation of course material for both popular and innovative topics in the discipline. It is hoped that prospective authors will welcome the opportunity to publish a structured and systematic presentation of some of the newer emerging control and signal processing technologies in the textbook series. An aim of Advanced Textbooks in Control and Signal Processing is to create a library that covers all the main subjects to be found in the control and signal pro- cessing fields. It is a growing but select series of high-quality books that now covers some fundamental topics and many more advanced topics in these areas. In trying to achieve a balanced library of course books, the Editors have long wished to have a text on system identification in the series. Although we often tend to think of system identification as a still-maturing subject, it is quite surprising to realise that the first International Federation of Automatic Control symposium on system identification was held as long ago as 1967 and that some of the classic textbooks on this topic were published during the 1970s and 1980s. Consequently, the existing literature and diversity of theory and applications areas is now quite extensive and provide a significant challenge to any prospective system identification course textbook au- thor. The Series Editors were therefore pleased to discover that Associate Professor Karel Keesman of Wageningen University in the Netherlands, was proposing to vii viii Series Editors’ Foreword take on this task and produce such a course textbook for the series entitled System Identification: An Introduction. We are now very pleased to welcome this finished textbook to the library of Advanced Textbooks in Control and Signal Processing. Although a wide literature exists for systems identification, there is a traditional classification of techniques into non-parametric and parametric methods, and Pro- fessor Keesman reflects this with Part I of his book focussed on the non-parametric methods, and Parts II and III emphasizing the parametric methods. Since every iden- tification practitioner wishes to know if the estimated model is a good model for the process, a novel feature of the textbook is Part IV that systematically presents a number of validation techniques for answering that very question. As befits a course textbook, the material develops in increasing technical depth as the reader progresses through the text, but there are starred sections to identify material that is more advanced technically or presents more recent technical devel- opments in the field. The presentational style is discursive with the integrated use of examples to illustrate technical and practical issues as they arise along the way. As part of this approach many different system examples have been used ranging from mechanical systems to more complex biological systems. Each chapter has a Problems section, and some solutions are available in the book. To support the math- ematical content (system identification involves elements of systems theory, matri- ces, statistics, transform methods (for example, Laplace and Fourier transforms), Bode diagrams, and shift operators), there are five accessible, short, focussed math- ematical appendices at the end of the book to aid the reader if necessary. This has the advantage of making the textbook fully self-contained for most readers. In terms of processes, Professor Keesman’s approach takes a broad view, and the textbook should be readily appreciated by readers from either the engineering or the scientific disciplines. Final-year undergraduate and graduate readers will find the book provides a stimulating tutorial-style entry to the field of system identification. For the established engineer or scientist, the mathematical level of the text and the supporting mathematical appendices will allow a speedy and insightful appreciation of the techniques of the field. This is an excellent addition to the Advanced Textbooks in Control and Signal Processing series. Industrial Control Centre M.J. Grimble Glasgow, Scotland, UK M.A. Johnson Preface St. Augustine of Hippo in De Civitate Dei writes ‘Si [···] fallor, sum’ (‘If I am mistaken, I am’) (book XI, 26) ‘I can therefore gladly admit that falsificationists like myself much prefer an attempt to solve an interesting problem by a bold conjecture, even (and especially) if it soon turns out to be false, to any recital of a sequence of irrelevant truisms. We prefer this because we believe that in this way we learn from our mistakes; and that in finding that our conjecture was false, we shall have learnt much about the truth, and shall have got nearer to the truth.’ POPPER, K. 1962 Conjectures and Refutations, New York: Basic Books, p. 231 ix x Preface Learning from mistakes, that according to Karl Popper brings us closer to the truth and, if prediction errors are interpreted as “mistakes”, it is the basic princi- ple underlying the majority of system identification methods. System identification aims at the construction of mathematical models from prior knowledge of the sys- tem under study and noisy time series data. Essentially, system identification is an art of modeling, where appropriate choices have to be made concerning the level of approximation given the final modeling objective and given noisy data. The scien- tific methods described in this book and obtained from statistics and system theory may help to solve the system identification problem in a systematic way. In gen- eral, system identification consists of three basic steps: experiment design and data acquisition, model structure selection and parameter estimation, and model vali- dation. In the past, many methods have been developed to support each of these steps. Initially, these methods were developed for each specific case. In the seven- ties, a more systematic approach to system identification arose with the start of the IFAC Symposia on Identification and System Parameter Estimation and the appear- ance of the books of Box and Jenkins on Time Series Analysis (1970), of Schweppe on Uncertain Dynamic Systems (1973) and Eykhoff’s book on System Identification (1974). Since then some ten books and many, most technical, papers have appeared on identification. Especially, the books of Norton entitled ‘An Introduction to Iden-