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Identification And Stochastic Adaptive Control
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Download or read book Stochastic Systems written by P. R. Kumar and published by SIAM. This book was released on 2015-12-15 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.?
Book Synopsis Identification and Stochastic Adaptive Control by : Han-fu Chen
Download or read book Identification and Stochastic Adaptive Control written by Han-fu Chen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.
Book Synopsis Identification and Stochastic Adaptive Control by : Hanfu Chen
Download or read book Identification and Stochastic Adaptive Control written by Hanfu Chen and published by . This book was released on 1991-01-01 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Systems: Estimation Identification and Adaptive Control by : Kumar P R
Download or read book Stochastic Systems: Estimation Identification and Adaptive Control written by Kumar P R and published by . This book was released on 1986 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Topics in Stochastic Systems: Modelling, Estimation and Adaptive Control by : L. Gerencser
Download or read book Topics in Stochastic Systems: Modelling, Estimation and Adaptive Control written by L. Gerencser and published by Springer. This book was released on 1991-07-25 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains a collection of survey papers in the areas of modelling, estimation and adaptive control of stochastic systems describing recent efforts to develop a systematic and elegant theory of identification and adaptive control. It is meant to provide a fast introduction to some of the recent achievements. The book is intended for graduate students and researchers interested in statistical problems of control in general. Students in robotics and communication will also find it valuable. Readers are expected to be familiar with the fundamentals of probability theory and stochastic processes.
Book Synopsis Stochastic Adaptive Control Results and Simulations by : Alexis Aloneftis
Download or read book Stochastic Adaptive Control Results and Simulations written by Alexis Aloneftis and published by Springer. This book was released on 2014-03-12 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theme of this monograph is the adaptive control of systems in a stochastic environment and, more precisely, the study of the tracking problem for ARMAX SISO stochastic systems with time invariant and time varying parameters. Results of simultaneous tracking and parameter identification are included. The author has aimed to (1) provide a reasonably self-contained and up-to-date exposition of the tracking problem after having properly placed it amongst numerous ideas, approaches, and subproblems related to adaptive control, (2) display computer simulation results and discuss their comparative behaviour, (3) introduce a new approach to the stochastic adaptive control with promising results, and (4) qualitatively discuss the adaptive control problem in the hope of improving our understanding of it, stimulate the informed reader to come up with new ideas, and attract newcomers to its study. The reader is assumed to have studied control systems at the graduate level and to have a reasonably good grasp of basic probability theory. Apart from its educational value to the adaptive control student, it is hoped that the accumulation of scattered results and their computer simulation, as well as an extensive reference section will attract the active researcher in this field.
Book Synopsis Topics in Stochastic Systems by : L Gerencser
Download or read book Topics in Stochastic Systems written by L Gerencser and published by Springer. This book was released on 2014-01-15 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Theory and Adaptive Control by : T.E. Duncan
Download or read book Stochastic Theory and Adaptive Control written by T.E. Duncan and published by Springer. This book was released on 1992-11-27 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This workshop on stochastic theory and adaptive control assembled many of the leading researchers on stochastic control and stochastic adaptive control to increase scientific exchange and cooperative research between these two subfields of stochastic analysis. The papers included in the proceedings include survey and research. They describe both theoretical results and applications of adaptive control. There are theoretical results in identification, filtering, control, adaptive control and various other related topics. Some applications to manufacturing systems, queues, networks, medicine and other topics are gien.
Book Synopsis Stochastic Theory and Adaptive Control by : T. E. Duncan
Download or read book Stochastic Theory and Adaptive Control written by T. E. Duncan and published by Springer. This book was released on 1992 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: This workshop on stochastic theory and adaptive control assembled many of the leading researchers on stochastic control and stochastic adaptive control to increase scientific exchange and cooperative research between these two subfields of stochastic analysis. The papers included in the proceedings include survey and research. They describe both theoretical results and applications of adaptive control. There are theoretical results in identification, filtering, control, adaptive control and various other related topics. Some applications to manufacturing systems, queues, networks, medicine and other topics are gien.
Book Synopsis Stochastic Convergence of algorithms for identification and adaptive control by : Lennart Ljung
Download or read book Stochastic Convergence of algorithms for identification and adaptive control written by Lennart Ljung and published by . This book was released on 1974 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Control by : Karl J. Åström
Download or read book Adaptive Control written by Karl J. Åström and published by Courier Corporation. This book was released on 2013-04-26 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Suitable for advanced undergraduates and graduate students, this overview introduces theoretical and practical aspects of adaptive control, with emphasis on deterministic and stochastic viewpoints. 1995 edition.
Book Synopsis Adaptive Control, Filtering, and Signal Processing by : K.J. Aström
Download or read book Adaptive Control, Filtering, and Signal Processing written by K.J. Aström and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: The area of adaptive systems, which encompasses recursive identification, adaptive control, filtering, and signal processing, has been one of the most active areas of the past decade. Since adaptive controllers are fundamentally nonlinear controllers which are applied to nominally linear, possibly stochastic and time-varying systems, their theoretical analysis is usually very difficult. Nevertheless, over the past decade much fundamental progress has been made on some key questions concerning their stability, convergence, performance, and robustness. Moreover, adaptive controllers have been successfully employed in numerous practical applications, and have even entered the marketplace.
Book Synopsis A Bridge Between Control Science and Technology: Identification, adaptive, and stochastic control by : International Federation of Automatic Control. World Congress
Download or read book A Bridge Between Control Science and Technology: Identification, adaptive, and stochastic control written by International Federation of Automatic Control. World Congress and published by . This book was released on 1985 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptive Filtering Prediction and Control by : Graham C Goodwin
Download or read book Adaptive Filtering Prediction and Control written by Graham C Goodwin and published by Courier Corporation. This book was released on 2014-05-05 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unified survey focuses on linear discrete-time systems and explores natural extensions to nonlinear systems. It emphasizes discrete-time systems, summarizing theoretical and practical aspects of a large class of adaptive algorithms. 1984 edition.
Book Synopsis Stochastic Theory and Adaptive Control by : T.E. Duncan
Download or read book Stochastic Theory and Adaptive Control written by T.E. Duncan and published by Springer. This book was released on 2014-03-12 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: This workshop on stochastic theory and adaptive control assembled many of the leading researchers on stochastic control and stochastic adaptive control to increase scientific exchange and cooperative research between these two subfields of stochastic analysis. The papers included in the proceedings include survey and research. They describe both theoretical results and applications of adaptive control. There are theoretical results in identification, filtering, control, adaptive control and various other related topics. Some applications to manufacturing systems, queues, networks, medicine and other topics are gien.
Book Synopsis Stochastic Adaptive System Theory for Identification, Filtering, Prediction and Control by : Wei Ren
Download or read book Stochastic Adaptive System Theory for Identification, Filtering, Prediction and Control written by Wei Ren and published by . This book was released on 1991 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the basic asymptotic properties of various stochastic adaptive systems for identification, filtering, prediction and control. These include the convergence of long-term averages of signals of interest (self-optimality), the convergence of adaptive filters or controllers (self-tuning property), the convergence of parameter estimates, and the rates of convergence. This thesis divides itself naturally into two parts. The first part considers identification, adaptive prediction and control based on the ARMAX model, while the second part considers general stochastic parallel model adaptation problems, which include output error identification, adaptive IIR filtering, adaptive noise cancelling, and adaptive feedforward control with or without input contamination. In the first part, the use of a generalized certainty equivalence approach in which the estimates of disturbance as well as parameters are utilized is proposed. Based on this, the self-optimality of adaptive minimum variance prediction and model reference adaptive control is established for systems with general delay and colored noise. Both direct and indirect approaches based on the extended least squares as well as the stochastic gradient algorithms are considered. For the direct approach, it is shown that interlacing is not necessary for convergence, thus resolving this long-standing open problem. Concerning the self-tuning property, it is established that self-optimality in the mean square sense, in general, implies self-tuning, by exhibiting the convergence of the parameter estimates to the null space of a certain covariance matrix, and by characterizing this null space. It is found that adaptive minimum variance regulators self-tune because of the "internal excitation" due to the plant disturbance alone. Finally, the exact order of external excitation required for the parameter estimates to converge to the true parameter is determined. In the second part of the thesis, the convergence of several parallel model adaptation schemes in the presence of nonstationary colored noise is established. A special case of our results resolves the long-standing problem of the convergence and unbiasedness of the output error identification scheme in the presence of colored noise. We also develop a simple general technique for analyzing the strong consistency of parameter estimation with projection. Of pedagogical interest is the deterministic reduction viewpoint we adopt in which all relevant properties of stochastically modeled disturbances are characterized deterministically by some long-term average properties. Readers more familiar with deterministic theory may well find this viewpoint to be more enlightening with respect to understanding the goals and results of stochastic adaptive system theory.
Book Synopsis Adaptive Algorithms and Stochastic Approximations by : Albert Benveniste
Download or read book Adaptive Algorithms and Stochastic Approximations written by Albert Benveniste and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive systems are widely encountered in many applications ranging through adaptive filtering and more generally adaptive signal processing, systems identification and adaptive control, to pattern recognition and machine intelligence: adaptation is now recognised as keystone of "intelligence" within computerised systems. These diverse areas echo the classes of models which conveniently describe each corresponding system. Thus although there can hardly be a "general theory of adaptive systems" encompassing both the modelling task and the design of the adaptation procedure, nevertheless, these diverse issues have a major common component: namely the use of adaptive algorithms, also known as stochastic approximations in the mathematical statistics literature, that is to say the adaptation procedure (once all modelling problems have been resolved). The juxtaposition of these two expressions in the title reflects the ambition of the authors to produce a reference work, both for engineers who use these adaptive algorithms and for probabilists or statisticians who would like to study stochastic approximations in terms of problems arising from real applications. Hence the book is organised in two parts, the first one user-oriented, and the second providing the mathematical foundations to support the practice described in the first part. The book covers the topcis of convergence, convergence rate, permanent adaptation and tracking, change detection, and is illustrated by various realistic applications originating from these areas of applications.
