Optimal experiment design for dynamic system identification

Optimal experiment design for dynamic system identification

Book Reviews A. !;sritsker and C. Pegden: Introduction to Simulation and SLAM. Wiley, New York, 1979, 588 pages. S. Rinaldi et al. : Modeling and Con...

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Book Reviews

A. !;sritsker and C. Pegden: Introduction to Simulation and SLAM. Wiley, New York, 1979, 588 pages. S. Rinaldi et al. : Modeling and Control of River Q~ality. McGraw-Hill, New York, 1979, 177 pages. W.Oo Vogt and M.H. Mickle (Editors): Modeling and Sim~dation, Volume 10. Proceedings of the 10th Ann.~al Pittsburg Conference; Published by Instrument Society of America. Distributor: Wiley, New York, 1979. The Annual Pittsburg Conference axled on the subject "Modeling and Simulation" finds every year an increased audience and the proceedings are every year more substantial. The proceedings are divided into 5 parts, each ore being the object of one book, and contain the 332 articles presented at the conference. Part I: Biomedical, 276 pages, £ 16.5, Part II: Systems and Control, 493 pages, £ 16.5, Part III: Energy and Environment, 572 pages, £ 16.5, Part IV: Socio-Economic Systems, 347 pages, £ 16.5, Part V: General Modelling and Simulation, 514 pages, £ 16.5. M.B~ Zarrop: Optimal Experiment Design for Dynamic System Identification. Springer-Verlag, Berlin, 1979, 197 pages.

1980 Z. Bubnicki: Identification of Control Plants. Elsevier Scientific Publishing Company, Amsterdam, 1980, 312 pages, US $ 68.25, Dfl. 140.00, ISBN 0-44499767°9. The rapid development of digital computers and systems, and their applications to process control, caused a tremendous growth of interest in the problems related to mathematical models of processes submitted to control (control plants), and thus in the problems of system identification, i.e. determination of mathematical models of plants for the purpose of control, on the basis of experimental data. The model of the process becomes the basis for designing control algorithms, and hence for setting up a control program library for the digital controlling system. Clearly, a good model will permit rational utilization of the capacities of the digital system.

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In the last few years, a great number of papers on system identification have appeared and many meth° ods and algorithms that are more or less useful in practice have been developed, in most cases drawing from the results of the theory of probability and mathematical statistics obtained in the twenties and thirties~ The present state of knowledge in the field of methodology of identification is far, however, from any uniform rationality. This book offers a unified treatment of the subject and attempts to introduce a certain order into the field of system identification. The book is i~tended primarily for research workers interested in problems of control arid identification, but may also be useful to a wide circle of specio lists interested in mathematical models, process identification and design of control systems. Contents: Chapter 1 : Goals and methods of identification. Ch. 2: Mathematical models of plants. Ch. 3: Static plant, deterministic problem. Ch. 4: Identification of static plant, probabilistic problem. Ch. 5: Identification of non-stationary plants. Ch. 6: Identification of dynamic properties, deterministic problem. Ch. 7 : Identification of dynamic properties, probabilistic problem. Ch. 8: Mathematical models and identification of complex plants. J.C. Howard: Practical Applications of Symbolic Computation. IPC Science and Technology Press, UK, 1980, 394 pages £ 20.00 or US $ 52.00; ISBN 0-86103-036-2. The techniques described in this book represent an attempt to combine the power of tensor calculus, symbolic computation, and computer science to formulate mathematical models in a variety of certain and curvilinear coordinate systems. In view of the fact that tensor calculus deals with entities and properties that are independent of the choice of reference frame, this combination may be used to advantage in reformulating practical problems, especially when these involve tedious and complex symbolic manipulations. As a consequence of the geometrical simplification inherent in the tensor method, the formulation of problems in different coordinate systems can be reduced to a series of routine operations involving only summation and differentiation. These operations can be performed manually, but for the benefit of those who have access to a digital computer equipped