Table Of ContentControl Engineering Series 52
Adaptive Prediction and PA Adaptive
rd
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Predictive Control a
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i Prediction and
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Cbeoenntr ohl igohftley ns ufoclcloewsssf upl riend picrtoiodnusc:i npgre rdoibcutivset acnodn tproral chtaicsa l PPraorftehsaso Pr raat ttihme IKnadniajnil aInl sitsit uatne Aosfs Toecciahtneo logy, P Predictive Control
solutions in many real-life, real-time applications. Adaptive Kharagpur. He is a graduate of the IIT and Cr
prediction covers a variety of ways of adding ‘intelligence’ to of the University of Sheffield. His research oe
interests include modelling and prediction
predictive control techniques. Many different groups, with d
of complex processes, linear and nonlinear n
widely varying disciplinary backgrounds and approaches,
time-series analysis, signal processing and i
are tackling the same problem from different angles; these predictive control. He has a particular interest tc
gfrroomup os thareer sdoismcipetliinmeess. unaware of alternative approaches iKna nthjiela la phpasli cparteiovniosu oslfy o wrtohrokgeodn aasl cao rnetsroela. rDchr roti
fellow at Oxford University, at CSIR and at lo
This book attempts to give a unified and comprehensive Tata Steel in India. He has published over 25
n
coverage of the principles and methods that these groups refereed papers in journals and international
have developed. It avoids basing its descriptions on conferences.
very complex mathematical formulations but still gives a
a rigorous exposure to the subject, and illustrates the n
theory with many practical examples. It is chiefly aimed
at students, researchers and practitioners, but will also be d P.P. Kanjilal
accessible to the non-specialist.
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The Institution of Engineering and Technology
www.theiet.org
0 86341 193 2
978-0-86341-193-9
IET conTrol EngInEErIng sErIEs 52
Series Editors: Professor D.P. Atherton
Professor G.I. Irwin
Adaptive
Prediction and
Predictive Control
Other volumes in this series:
Volume 2 Elevator traffic analysis, design and control, 2nd edition G.C. Barney and
S.M. dos Santos
Volume 8 A history of control engineering, 1800–1930 S. Bennett
Volume 14 Optimal relay and saturating control system synthesis E.P. Ryan
Volume 18 Applied control theory, 2nd edition J.R. Leigh
Volume 20 Design of modern control systems D.J. Bell, P.A. Cook and N. Munro (Editors)
Volume 28 Robots and automated manufacture J. Billingsley (Editor)
Volume 32 Multivariable control for industrial applications J. O’Reilly (Editor)
Volume 33 Temperature measurement and control J.R. Leigh
Volume 34 Singular perturbation methodology in control systems D.S. Naidu
Volume 35 Implementation of self-tuning controllers K. Warwick (Editor)
Volume 37 Industrial digital control systems, 2nd edition K. Warwick and D. Rees (Editors)
Volume 39 Continuous time controller design R. Balasubramanian
Volume 40 Deterministic control of uncertain systems A.S.I. Zinober (Editor)
Volume 41 Computer control of real-time processes S. Bennett and G.S. Virk (Editors)
Volume 42 Digital signal processing: principles, devices and applications N.B. Jones
and J.D.McK. Watson (Editors)
Volume 44 Knowledge-based systems for industrial control J. McGhee, M.J. Grimble and
A. Mowforth (Editors)
Volume 47 A history of control engineering, 1930–1956 S. Bennett
Volume 49 Polynomial methods in optimal control and filtering K.J. Hunt (Editor)
Volume 50 Programming industrial control systems using IEC 1131-3 R.W. Lewis
Volume 51 Advanced robotics and intelligent machines J.O. Gray and D.G. Caldwell
(Editors)
Volume 52 Adaptive prediction and predictive control P.P. Kanjilal
Volume 53 Neural network applications in control G.W. Irwin, K. Warwick and K.J. Hunt
(Editors)
Volume 54 Control engineering solutions: a practical approach P. Albertos, R. Strietzel
and N. Mort (Editors)
Volume 55 Genetic algorithms in engineering systems A.M.S. Zalzala and P.J. Fleming
(Editors)
Volume 56 Symbolic methods in control system analysis and design N. Munro (Editor)
Volume 57 Flight control systems R.W. Pratt (Editor)
Volume 58 Power-plant control and instrumentation D. Lindsley
Volume 59 Modelling control systems using IEC 61499 R. Lewis
Volume 60 People in control: human factors in control room design J. Noyes and
M. Bransby (Editors)
Volume 61 Nonlinear predictive control: theory and practice B. Kouvaritakis and
M. Cannon (Editors)
Volume 62 Active sound and vibration control M.O. Tokhi and S.M. Veres
Volume 63 Stepping motors: a guide to theory and practice, 4th edition P.P. Acarnley
Volume 64 Control theory, 2nd edition J.R. Leigh
Volume 65 Modelling and parameter estimation of dynamic systems J.R. Raol, G. Girija
and J. Singh
Volume 66 Variable structure systems: from principles to implementation
A. Sabanovic, L. Fridman and S. Spurgeon (Editors)
Volume 67 Motion vision: design of compact motion sensing solution for autonomous
systems J. Kolodko and L. Vlacic
Volume 68 Flexible robot manipulators: modelling, simulation and control M.O. Tokhi
and A.K.M. Azad (Editors)
Volume 69 Unmanned marine vehicles G. Roberts and R. Sutton (Editors)
Volume 70 Intelligent control systems using computational intelligence techniques
A. Ruano (Editor)
Adaptive
Prediction and
Predictive Control
P.P. Kanjilal
The Institution of Engineering and Technology
Published by The Institution of Engineering and Technology, London, United Kingdom
First edition © 1995 Peter Peregrinus Ltd
Reprint with new cover © 2008 The Institution of Engineering and Technology
First published 1995
Reprinted 2008
This publication is copyright under the Berne Convention and the Universal Copyright
Convention. All rights reserved. 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 be reproduced, stored or transmitted, in any
form or by any means, only 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. Inquiries concerning reproduction outside those
terms should be sent to the publishers at the undermentioned address:
The Institution of Engineering and Technology
Michael Faraday House
Six Hills Way, Stevenage
Herts, SG1 2AY, United Kingdom
www.theiet.org
While the author and the publishers believe that the information and guidance given
in this work are correct, all parties must rely upon their own skill and judgement when
making use of them. Neither the author nor the publishers assume any liability to
anyone for any loss or damage caused by any error or omission in the work, whether
such error or omission is the result of negligence or any other cause. Any and all such
liability is disclaimed.
The moral rights of the author to be identified as author of this work have been
asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
British Library Cataloguing in Publication Data
A CIP catalogue record for this product is available from the British Library
ISBN (10 digit) 0 86341 193 2
ISBN (13 digit) 978-0-86341-193-9
Printed in the UK by Short Run Press Ltd, Exeter
Reprinted in the UK by Lightning Source UK Ltd, Milton Keynes
To my parents
Bina and Gopal Chandra Kanjilal,
to my wife
Sagarika,
and to our children
Debayan and Sreyashi
CONTENTS
Preface xv
1 INTRODUCTION 1
2 PROCESS MODELS 9
2.1 Introduction 9
2.2 Process Models and their Choice 10
2.2.1 Classes of models 10
2.2.2 Choice of models 16
2.3 Stochastic Processes 17
2.3.1 Basic concepts and processes 18
2.3.2 Examples of common processes 22
2.4 Transfer-function Models 25
2.4.1 Some basic models 25
2.4.2 Model structures 28
2.4.3 Other models 31
2.5 Models Based On Frequency Domain Analysis 36
2.5.1 Representation of a periodic signal and
the Fourier series 37
2.5.2 Representation of a nonperiodic signal and
the Fourier transform 40
2.5.3 Discrete-time signals and their Fourier
transform 44
2.5.4 Modelling of a periodic signal 50
2.6 Structural Modelling 51
2.6.1 A basic model 52
2.6.2 Models with multiple periodic components 52
2.7 Concluding Remarks 53
References 54
3 PARAMETER ESTIMATION 56
3.1 Introduction 56
3.2 Linear Regression and the Least Squares Method 58
3.2.1 Formulation of LS estimator 60
3.2.2 Features and properties 61
3.3 LS Estimation: Computational Aspects 64
3.3.1 Solving normal equations 65
3.3.2 Orthogonal LS estimation 66
vn
viii Contents
3.3.3 Rank deficient LS estimation 70
3.3.4 Estimation with orthogonalized regressors 76
3.4 Recursive Least Squares Method 79
3.4.1 RLS formulation 79
3.4.2 Implementation aspects 81
3.5 Some Selected Methods: An Introduction 83
3.5.1 Instrumental variable method 84
3.5.2 Maximum likelihood method 86
3.5.3 The Koopmans-Levin method:
implemented using SVD 88
3.6 Model Selection and Validation 92
3.6.1 Akaike information criterion (AIC) 93
3.6.2 Subset selection from an
information set 94
3.6.3 Case study: Best subset-AR modelling using
information criterion and subset selection
96
3.6.4 Linear Regression through subset selection
101
3.6.5 Cross validation 105
3.7 Conclusions 106
References 107
4 SOME POPULAR METHODS OF PREDICTIONS 111
4.1 Introduction 111
4.2 Smoothing Methods of Prediction 112
4.2.1 Basic smoothing methods 112
4.2.2 Multiple smoothing algorithms 115
4.3 Box and Jenkins Method 118
4.3.1 Modelling characteristics 118
4.3.2 Implementation aspects 126
4.4 Other Selected Methods 127
4.5 Concluding Remarks 130
References 131
5 ADAPTIVE PREDICTION USING TRANSFER-FUNCTION
MODELS 133
5.1 Introduction 133
5.2 Minimum Mean Square Error Prediction 134
5.2.1 Explicit (indirect) prediction 136
5.2.2 Implicit (direct) prediction 139
Contents ix
5.3 Constrained Mean Square Error Prediction 141
5.3.1 Why constrain prediction 141
5.3.2 Cost criteria 142
5.3.3 Prediction formulations 145
5.3.4 Comparative study 149
5.4 Multistep Prediction Through Process Model
Recursion 150
5.5 -Case Study: Prediction of Product Quality in
Iron-ore Sintering Process 152
5.5.1 Process description and prediction
problem 152
5.5.2 Data preparation 154
5.5.3 Prediction exercise 155
5.6 Conclusions 157
References 158
KALMAN FILTER AND STATE-SPACE APPROACHES 160
6.1 Introduction 160
6.2 State-space Representation 161
6.3 State Equations from Difference Equation
Models 163
6.3.1 Processes without measurement noise 163
6.3.2 Processes with noise 167
6.4 State-space Models for Periodic Processes 171
6.4.1 Trend model 171
6.4.2 Periodic component model 173
6.4.3 Prediction problem formulation 178
6.5 Optimal State Estimation 179
6.6 The Kalman Filter 180
6.6.1 The estimation problem 181
6.6.2 Kalman filter equations 182
6.6.3 Properties and salient features 184
6.6.4 Implementation aspects 186
6.7 Optimal Prediction 187
6.8 Case Study: Estimation and Prediction of Ingot
Temperatures and Heating in Soaking Pits
189
6.8.1 Process description and problem
statement 189
6.8.2 Modelling, estimation and prediction 192
6.9 Concluding Remarks 196
References 197
