Model Structure Estimation In Identification And Adaptive Control
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System Identification and Adaptive Control
| Author | : Yiannis Boutalis |
| Publisher | : Springer Science & Business |
| Total Pages | : 316 |
| Release | : 2014-04-23 |
| Genre | : Technology & Engineering |
| ISBN | : 3319063642 |
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.
Identification and Stochastic Adaptive Control
| Author | : Han-fu Chen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 436 |
| Release | : 2012-12-06 |
| Genre | : Science |
| ISBN | : 1461204291 |
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.
Fuzzy System Identification and Adaptive Control
| Author | : Ruiyun Qi |
| Publisher | : Springer |
| Total Pages | : 293 |
| Release | : 2019-06-11 |
| Genre | : Technology & Engineering |
| ISBN | : 3030198820 |
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 form T–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.
Model Identification and Adaptive Control
| Author | : Graham Goodwin |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 302 |
| Release | : 2012-12-06 |
| Genre | : Technology & Engineering |
| ISBN | : 144710711X |
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.
Stochastic Systems
| Author | : P. R. Kumar |
| Publisher | : SIAM |
| Total Pages | : 371 |
| Release | : 2015-12-15 |
| Genre | : Mathematics |
| ISBN | : 1611974259 |
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.
Control of a Large Space Structure Using Multiple Model Adaptive Estimation and Control Techniques
| Author | : |
| Publisher | : |
| Total Pages | : 314 |
| Release | : 1993 |
| Genre | : |
| ISBN | : |
The purpose of this thesis is to apply moving-bank multiple model adaptive estimation and control (MMAE/MMAC) algorithms to an actual space structure (SPICE) being examined at Phillips Laboratory at Kirtland AFB, NM. The structure consists of a large platform and a smaller platform connected by three legs in a tripod fashion. Kalman filtering and LQG control techniques are utilized as the primary design tool. Implementing a bank of filters increases the robustness of the LQG controller when uncertainties exist in the system model, whereas the moving bank is utilized to reduce the computational load. Several reduced-order models are developed from the truth model using modal analysis and modal cost analysis. The MMAE/MMAC design with a dramatically reduced-order filter model provides an excellent method to estimate a wide range of parameter variations and to quell oscillations in the structure. Multiple model adaptive estimation, Multiple model adaptive control, LQG Control, Flexible space structure, Parameter identification.
Adaptive Control for Partially Known Systems
| Author | : Carlos A. Canudas de Wit |
| Publisher | : Elsevier Publishing Company |
| Total Pages | : 292 |
| Release | : 1988 |
| Genre | : Adaptive control systems |
| ISBN | : |
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.
Iterative Identification and Control
| Author | : Pedro Albertos |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 320 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1447102053 |
An exposition of the interplay between the modelling of dynamic systems and the design of feedback controllers based on these models. The authors of individual chapters are some of the most renowned and authoritative figures in the fields of system identification and control design.
Robust Adaptive Control
| Author | : Petros Ioannou |
| Publisher | : Courier Corporation |
| Total Pages | : 850 |
| Release | : 2013-09-26 |
| Genre | : Technology & Engineering |
| ISBN | : 0486320723 |
Presented in a tutorial style, this comprehensive treatment unifies, simplifies, and explains most of the techniques for designing and analyzing adaptive control systems. Numerous examples clarify procedures and methods. 1995 edition.
