System Identification and Adaptive Control

System Identification and Adaptive Control
Author: Yiannis Boutalis
Publisher: Springer Science & Business
Total Pages: 316
Release: 2014-04-23
Genre: Technology & Engineering
ISBN: 3319063642
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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.

Adaptive Control for Partially Known Systems

Adaptive Control for Partially Known Systems
Author: Carlos A. Canudas de Wit
Publisher: Elsevier Publishing Company
Total Pages: 292
Release: 1988
Genre: Technology & Engineering
ISBN:
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Adaptive control has been considered as an alternative in designing high-performance control systems, from the beginning of the 1950s. Since then, most of the adaptive control schemes have been formulated either in the continuous-time or in the discrete-time framework. Both approaches commonly use black-box'' models for describing the process to be controlled; models with known structure but unknown parameters. These models have the advantage that they are general but also the disadvantage that many parameters have to be estimated. There are in practice, however, many adaptive problems where the system can be described as partially known in the sense that part of the system dynamics is known and another part unknown. This is the kind of system considered in this book. Most of the adaptive algorithms that are reliable - in the sense that they guarantee closed-loop stability and some performance behaviour - require to a certain extent some system knowledge and a checking procedure for the caution update of the parameter estimates.

Nonlinear and Adaptive Control

Nonlinear and Adaptive Control
Author: Alessandro Astolfi
Publisher: World Scientific
Total Pages: 313
Release: 2006
Genre: Technology & Engineering
ISBN: 1860946178
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This book summarizes the main results achieved in a four-year European Project on nonlinear and adaptive control. The project involves leading researchers from top-notch institutions: Imperial College London (Prof A Astolfi), Lund University (Prof A Rantzer), Supelec Paris (Prof R Ortega), University of Technology of Compiegne (Prof R Lozano), Grenoble Polytechnic (Prof C Canudas de Wit), University of Twente (Prof A van der Schaft), Politecnico of Milan (Prof S Bittanti), and Polytechnic University of Valencia (Prof P Albertos).The book also provides an introduction to theoretical advances in nonlinear and adaptive control and an overview of novel applications of advanced control theory, particularly topics on the control of partially known systems, under-actuated systems, and bioreactors.

Adaptive Control, Filtering, and Signal Processing

Adaptive Control, Filtering, and Signal Processing
Author: K.J. Aström
Publisher: Springer Science & Business Media
Total Pages: 404
Release: 2012-12-06
Genre: Science
ISBN: 1441985689
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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.

Model Free Adaptive Control

Model Free Adaptive Control
Author: Zhongsheng Hou
Publisher: CRC Press
Total Pages: 400
Release: 2013-09-24
Genre: Technology & Engineering
ISBN: 1466594187
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Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and output data of the controlled plant, which makes it more applicable for many practical plants. This book covers new concepts, including pseudo partial derivative, pseudo gradient, pseudo Jacobian matrix, and generalized Lipschitz conditions, etc.; dynamic linearization approaches for nonlinear systems, such as compact-form dynamic linearization, partial-form dynamic linearization, and full-form dynamic linearization; a series of control system design methods, including MFAC prototype, model-free adaptive predictive control, model-free adaptive iterative learning control, and the corresponding stability analysis and typical applications in practice. In addition, some other important issues related to MFAC are also discussed. They are the MFAC for complex connected systems, the modularized controller designs between MFAC and other control methods, the robustness of MFAC, and the symmetric similarity for adaptive control system design. The book is written for researchers who are interested in control theory and control engineering, senior undergraduates and graduated students in engineering and applied sciences, as well as professional engineers in process control.

Adaptive Systems in Control and Signal Processing 1992

Adaptive Systems in Control and Signal Processing 1992
Author: L. Dugard
Publisher: Elsevier
Total Pages: 557
Release: 2014-06-28
Genre: Technology & Engineering
ISBN: 1483298809
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Adaptive Systems remain a very interesting field of theoretical research, extended by methodological studies and an increasing number of applications. The plenary papers, invited sessions and contributed sessions focused on many aspects of adaptive systems, such as systems identification and modelling, adaptive control of nonlinear systems and theoretical issues in adaptive control. Also covered were methodological aspects and applications of adaptive control, intelligent tuning and adaptive signal processing.

Adaptive Control Systems

Adaptive Control Systems
Author: Chalam
Publisher: Routledge
Total Pages: 391
Release: 2017-10-19
Genre: Technology & Engineering
ISBN: 1351468979
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impossible to access. It has been widely scattered in papers, reports, and proceedings ofsymposia, with different authors employing different symbols and terms. But now thereis a book that covers all aspects of this dynamic topic in a systematic manner.Featuring consistent terminology and compatible notation, and emphasizing unifiedstrategies, Adaptive Control Systems provides a comprehensive, integrated accountof basic concepts, analytical tools, algorithms, and a wide variety of application trendsand techniques.Adaptive Control Systems deals not only with the two principal approachesmodelreference adaptive control and self-tuning regulators-but also considers otheradaptive strategies involving variable structure systems, reduced order schemes, predictivecontrol, fuzzy logic, and more. In addition, it highlights a large number of practical applicationsin a range of fields from electrical to biomedical and aerospace engineering ...and includes coverage of industrial robots.The book identifies current trends in the development of adaptive control systems ...delineates areas for further research . : . and provides an invaluable bibliography of over1,200 references to the literature.The first authoritative reference in this important area of work, Adaptive ControlSystems is an essential information source for electrical and electronics, R&D,chemical, mechanical, aerospace, biomedical, metallurgical, marine, transportation, andpower plant engineers. It is also useful as a text in professional society seminars and inhousetraining programs for personnel involved with the control of complex systems, andfor graduate students engaged in the study of adaptive control systems.

Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles
Author: Draguna L. Vrabie
Publisher: IET
Total Pages: 305
Release: 2013
Genre: Computers
ISBN: 1849194890
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The book reviews developments in the following fields: optimal adaptive control; online differential games; reinforcement learning principles; and dynamic feedback control systems.

Learning-Based Adaptive Control

Learning-Based Adaptive Control
Author: Mouhacine Benosman
Publisher: Butterworth-Heinemann
Total Pages: 284
Release: 2016-08-02
Genre: Technology & Engineering
ISBN: 0128031514
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Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. Includes a good number of Mechatronics Examples of the techniques. Compares and blends Model-free and Model-based learning algorithms. Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.