Download or read book Adaptive Control and Identification of Linear Systems written by Howard Elliott and published by . This book was released on 1978 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Identification of Linear Systems by Transient Response Method written by Kanti C. Shah and published by . This book was released on 1965 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book System Identification for Self adaptive Control written by W. D. T. Davies and published by John Wiley & Sons. This book was released on 1970 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Linear Systems Identification and Optimization with Application to Adaptive Control written by Adel Abdel Raouf Hanafy and published by . This book was released on 1972 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

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.?

Download or read book Adaptive Control written by Ioan Doré Landau and published by Springer Science & Business Media. This book was released on 2011-06-06 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive Control (second edition) shows how a desired level of system performance can be maintained automatically and in real time, even when process or disturbance parameters are unknown and variable. It is a coherent exposition of the many aspects of this field, setting out the problems to be addressed and moving on to solutions, their practical significance and their application. Discrete-time aspects of adaptive control are emphasized to reflect the importance of digital computers in the application of the ideas presented. The second edition is thoroughly revised to throw light on recent developments in theory and applications with new chapters on: multimodel adaptive control with switching, direct and indirect adaptive regulation and adaptive feedforward disturbance compensation. Many algorithms are newly presented in MATLAB® m-file format to facilitate their employment in real systems. Classroom-tested slides for instructors to use in teaching this material are also now provided. All of this supplementary electronic material can be downloaded from fill in URL. The core material is also up-dated and re-edited to keep its perspective in line with modern ideas and more closely to associate algorithms with their applications giving the reader a solid grounding in: synthesis and analysis of parameter adaptation algorithms, recursive plant model identification in open and closed loop, robust digital control for adaptive control; • robust parameter adaptation algorithms, practical considerations and applications, including flexible transmission systems, active vibration control and broadband disturbance rejection and a supplementary introduction on hot dip galvanizing and a phosphate drying furnace. Control researchers and applied mathematicians will find Adaptive Control of significant and enduring interest and its use of example and application will appeal to practitioners working with unknown- and variable-parameter plant. Praise for the first edition: ...well written, interesting and easy to follow, so that it constitutes a valuable addition to the monographies in adaptive control for discrete-time linear systems... suitable (at least in part) for use in graduate courses in adaptive control.

Download or read book Reliable Control and Filtering of Linear Systems with Adaptive Mechanisms written by Guang-Hong Yang and published by CRC Press. This book was released on 2018-09-03 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: More and more, the advanced technological systems of today rely on sophisticated control systems designed to assure greater levels of safe operation while optimizing performance. Rather than assuming always perfect conditions, these systems require adaptive approaches capable of coping with inevitable system component faults. Conventional feedback control designs do not offer that capability and can result in unsatisfactory performance or even instability, which is totally unacceptable in complex systems such as aircraft, spacecraft, and nuclear power plants where safety is a paramount concern. Reliable Control and Filtering of Linear Systems with Adaptive Mechanisms presents recent research results that are advancing the field. It shows how adaptive mechanisms can be successfully introduced into the traditional reliable control/filtering, so that, based on the online estimation of eventual faults, the proposed adaptive reliable controller/filter parameters are updated automatically to compensate for any fault effects. Presenting a new method for fault-tolerant control (FTC) in the context of existing research, this uniquely cohesive volume, coauthored by two leading researchers — Focuses on the issues of reliable control/filtering in the framework of indirect adaptive method and LMI techniques Starts from the development and main research methods in FTC to offer a systematic presentation of new methods for adaptive reliable control/filtering of linear systems Explains the principles behind adaptive designs for closed-loop systems in normal operation as well as those that account for both actuator and sensor failures Presents rigorous mathematical analysis of control methods as well as easy-to-implement algorithms Includes practical case studies derived from the aerospace industry including simulation results for the F-16 The authors also extend the design idea from linear systems to linear time-delay systems via both memory and memory-less controllers. Moreover, some more recent results for the corresponding adaptive reliable control against actuator saturation are included. Ultimately, this remarkably practical resource, offers design approaches and guidelines that researchers can readily employ in the design of advanced FTC techniques offering improved reliability, maintainability, and survivability.

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.

Download or read book Subspace Identification for Linear Systems written by Peter van Overschee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering.

Download or read book Adaptive Control with Recursive Identification for Stochastic Linear Systems written by Stéphane Lafortune and published by . This book was released on 1982 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Delay Adaptive Linear Control written by Yang Zhu and published by Princeton University Press. This book was released on 2020-04-28 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Basic predictor feedback for single-input systems -- Basic idea of adaptive control for single-input systems -- Single-input systems with full relative degree -- Single-input systems with arbitrary relative degree -- Exact predictor feedback for multi-input systems -- Full-state feedback of uncertain multi-input systems -- Output feedback of uncertain multi-input systems -- Output feedback of systems with uncertain delays, parameters and ODE state -- Predictor feedback for uncertainty-free systems -- Predictor feedback of uncertain single-input systems -- Predictor feedback of uncertain multi-input systems.

Download or read book Model Identification and Adaptive Control written by Graham Goodwin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is based on a workshop entitled.: Model " Identification and Adap tive Control: From Windsurfing to Telecommunications" held in Sydney, Aus tralia, on December 16, 2000. The workshop was organized in honour of Pro fessor Brian (BDO) Anderson in recognition of his seminal contributions to systems science over the past 4 decades. . The chapters in the book have been written by colleagues, friends and stu dents of Brian Anderson. A central theme of the book is the inter relationship between identification and the use of models in real world applications. This theme has underpinned much of Brian Anderson's own contributions. The book reflects on these contributions as well as makirig important statements about possible future research directions. The subtitle of the book (From Windsurfing to Telecommunications) rec ognizes the fact that many common life experiences, such as those we en counter when learning to ride a windsurfer are models for design methods that can be used on real world advanced technological control problems. In deed, Brian Anderson extensively explored this link in his research work.

Download or read book SYSTEM IDENTIFICATION USING ADAPTIVE CONTROL SYSTEMS written by Dr. SHAIK RAFI KIRAN and published by Lulu.com. This book was released on with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book System Identification and Adaptive Control written by Yiannis Boutalis and published by Springer Science & Business. This book was released on 2014-04-23 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: • contemporary power generation; • process control and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results.

Download or read book Adaptive Control Review Vol Ii Limits on the Identification Time for Linear Systems written by G. R. Cooper and published by . This book was released on 1965 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work on adaptive control considers the problem of determining the time that is necessary to estimate the impulse response of a linear system with a specified variance. The results show that, when the external disturbance is Gaussian and white, and the output signal energy is large compared to the power spectral density of the noise, the identification time is proportional to the power spectral density of the noise, and inversely proportional to the variance of the estimate and the mean square value of the input test signal. It is also concluded that, when the noise is white and the test signal is optimized, no measurement technique will yield a smaller identification time than that of the ideal identifier. (Author).