Stability and stabilization of infinite dimensional systems with applications: Zheng-Hua Luo, Bao-Zhu Guo, Omer Morgul, Springer, London, 1999, ISBN: 1-85233-124-0

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. 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. Preface Numerous papers on system identification have been published over the last 40 years. Though there were substantial developments in the theory of stationary stochastic processes and multivariable statistical methods during 1950s, it is widely recognized that the theory of system identification started only in the mid-1960s with the publication of two important papers; one due toÅström and Bohlin [17], in which the maximum likelihood (ML) method was extended to a serially correlated time series to estimate ARMAX models, and the other due to Ho and Kalman [72], in which the deterministic state space realization problem was solved for the first time using a certain Hankel matrix formed in terms of impulse responses. These two papers have laid the foundation for the future developments of system identification theory and techniques [55]. The scope of the ML identification method ofÅström and Bohlin [17] was to build single-input, single-output (SISO) ARMAX models from observed input-output data sequences. Since the appearance of their paper, many statistical identification techniques have been developed in the literature, most of which are now comprised under the label of prediction error methods (PEM) or instrumental variable (IV) methods. This has culminated in the publication of the volumes Ljung [109] and Söderström and Stoica [145]. At this moment we can say that theory of system identification for SISO systems is established, and the various identification algorithms have been well tested, and are now available as MATLAB R ­ programs. Also, …