System Identification And Adaptive Control
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System Identification And Adaptive Control
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Author : Bahram Shafai
language : en
Publisher: Springer
Release Date : 2012-04-30
System Identification And Adaptive Control written by Bahram Shafai and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-30 with Technology & Engineering categories.
This book offers comprehensive coverage of identification and adaptive control while familiarizing graduate students and practicing engineers with computational software tools such as MATLAB and SIMULINK and describing the underlying theoretical concepts. Identification is the process of mathematically modeling a system based on measurement data that may be limited or uncertain. Adaptive control is the means whereby a system that is poorly modeled is controlled adequately. Therefore the topical coverage is divided into two parts: Part I describes fundamental topics of system identification independent of adaptive control and discusses nonparametric and parameteric estimation methods while emphasizing least squares techniques instrumental variables and prediction error methods. Part II describes various methods of adaptive control in which the materials discussed in Part I are essential for control purposes, including model reference, adaptive control and self-tuning regulators.
System Identification For Self Adaptive Control
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Author : W. D. T. Davies
language : en
Publisher: John Wiley & Sons
Release Date : 1970
System Identification For Self Adaptive Control written by W. D. T. Davies and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 1970 with Technology & Engineering categories.
System Identification And Adaptive Control
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Author : Yiannis Boutalis
language : en
Publisher: Springer Science & Business
Release Date : 2014-04-23
System Identification And Adaptive Control written by Yiannis Boutalis and has been published by Springer Science & Business this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-23 with Technology & Engineering categories.
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.
Fuzzy System Identification And Adaptive Control
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Author : Ruiyun Qi
language : en
Publisher: Springer
Release Date : 2019-06-11
Fuzzy System Identification And Adaptive Control written by Ruiyun Qi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-11 with Technology & Engineering categories.
This book provides readers with a systematic and unified framework for identification and adaptive control of Takagi–Sugeno (T–S) fuzzy systems. Its design techniques help readers applying these powerful tools to solve challenging nonlinear control problems. The book embodies a systematic study of fuzzy system identification and control problems, using T–S fuzzy system tools for both function approximation and feedback control of nonlinear systems. Alongside this framework, the book also: introduces basic concepts of fuzzy sets, logic and inference system; discusses important properties of T–S fuzzy systems; develops offline and online identification algorithms for T–S fuzzy systems; investigates the various controller structures and corresponding design conditions for adaptive control of continuous-time T–S fuzzy systems; develops adaptive control algorithms for discrete-time input–output formT–S fuzzy systems with much relaxed design conditions, and discrete-time state-space T–S fuzzy systems; and designs stable parameter-adaptation algorithms for both linearly and nonlinearly parameterized T–S fuzzy systems. The authors address adaptive fault compensation problems for T–S fuzzy systems subject to actuator faults. They cover a broad spectrum of related technical topics and to develop a substantial set of adaptive nonlinear system control tools. Fuzzy System Identification and Adaptive Control helps engineers in the mechanical, electrical and aerospace fields, to solve complex control design problems. The book can be used as a reference for researchers and academics in nonlinear, intelligent, adaptive and fault-tolerant control.
System Identification And Adaptive Control
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Author :
language : en
Publisher:
Release Date : 1985
System Identification And Adaptive Control written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with Adaptive control systems categories.
System Identification 2003
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Author : Paul Van Den Hof
language : en
Publisher: Elsevier
Release Date : 2004-06-29
System Identification 2003 written by Paul Van Den Hof and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-29 with Technology & Engineering categories.
The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems*Provides the latest research on System Identification*Contains contributions written by experts in the field*Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.
Identification And Stochastic Adaptive Control
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Author : Han-Fu Chen
language : en
Publisher:
Release Date : 1991-11-01
Identification And Stochastic Adaptive Control written by Han-Fu Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-11-01 with categories.
Identification And Stochastic Adaptive Control
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Author : Hanfu Chen
language : en
Publisher:
Release Date : 1991-01-01
Identification And Stochastic Adaptive Control written by Hanfu Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991-01-01 with Adaptive control systems categories.
System Identification And Adaptive Control
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Author : Byron A. Brasseux
language : en
Publisher:
Release Date : 1990
System Identification And Adaptive Control written by Byron A. Brasseux and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Adaptive control systems categories.
Stochastic Systems
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Author : P. R. Kumar
language : en
Publisher: SIAM
Release Date : 2015-12-15
Stochastic Systems written by P. R. Kumar and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-15 with Mathematics categories.
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.