Hybrid Adaptive Control Using the Inverse System PDF Download

Author: Sadashiva Shankar Godbole
Publisher:
ISBN:
Category :
Languages : en
Pages : 198

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Author: Petros Ioannou
Publisher: Courier Corporation
ISBN: 0486320723
Category : Technology & Engineering
Languages : en
Pages : 850

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Book Description
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.

Author: Kumpati S. Narendra
Publisher:
ISBN:
Category :
Languages : en
Pages : 27

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Book Description
The paper deals with hybrid adaptive control of single-input single-output linear dynamical systems with unknown parameters. The system operates in continuous time while control parameters updated only at discrete instants. Using a hybrid error model it is shown that adaptive algorithms used in discrete and continuous systems can be directly extended to hybrid systems. The resulting nonlinear time-varying systems are globally stable and independent of the frequency with which the parameters are adjusted.

Author: Najim Abdul-Hadi AL-Hamdan AL-Abdullah
Publisher:
ISBN:
Category :
Languages : en
Pages : 350

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Author: Gang Tao
Publisher: Wiley-Interscience
ISBN:
Category : Mathematics
Languages : en
Pages : 320

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Book Description
The authors present an effective approach to handle some of the most common types of component imperfections encountered in industrial automation, consumer electroncis, and defence and transportation systems.

Author: Petros A. Ioannou
Publisher: Courier Corporation
ISBN: 0486498174
Category : Technology & Engineering
Languages : en
Pages : 850

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Book Description
" Presented in a tutorial style, this text reduces the confusion and difficulty in grasping the design, analysis, and robustness of a wide class of adaptive controls for continuous-time plants. The treatment unifies, simplifies, and explains most of the techniques for designing and analyzing adaptive control systems. Excellent text and authoritative reference"--

Author: Bernard Widrow
Publisher: John Wiley & Sons
ISBN: 9780470231609
Category : Technology & Engineering
Languages : en
Pages : 544

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Book Description
A self-contained introduction to adaptive inverse control Now featuring a revised preface that emphasizes the coverage of both control systems and signal processing, this reissued edition of Adaptive Inverse Control takes a novel approach that is not available in any other book. Written by two pioneers in the field, Adaptive Inverse Control presents methods of adaptive signal processing that are borrowed from the field of digital signal processing to solve problems in dynamic systems control. This unique approach allows engineers in both fields to share tools and techniques. Clearly and intuitively written, Adaptive Inverse Control illuminates theory with an emphasis on practical applications and commonsense understanding. It covers: the adaptive inverse control concept; Weiner filters; adaptive LMS filters; adaptive modeling; inverse plant modeling; adaptive inverse control; other configurations for adaptive inverse control; plant disturbance canceling; system integration; Multiple-Input Multiple-Output (MIMO) adaptive inverse control systems; nonlinear adaptive inverse control systems; and more. Complete with a glossary, an index, and chapter summaries that consolidate the information presented, Adaptive Inverse Control is appropriate as a textbook for advanced undergraduate- and graduate-level courses on adaptive control and also serves as a valuable resource for practitioners in the fields of control systems and signal processing.

Author: Roberto Cristi
Publisher:
ISBN:
Category : Adaptive control systems
Languages : en
Pages : 282

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Book Description
In this report, an algorithm for adaptive control of continuous time single-input single-output systems is presented. With the hybrid approach, the control structure involves a continuous as well as a discrete time part, instead of being totally discrete or totally continuous as in previous approaches. The system is sampled and the adaptive gains updated at a variable rate varying with the magnitude of the error itself.

Author: Carlos A. Canudas de Wit
Publisher: Elsevier Publishing Company
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 292

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Book Description
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.

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 112

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Book Description
Connectionist learning systems are function approximation systems which learn from examples, and have received an increase in interest in recent years. They have been found useful for a number of tasks, including control of high dimensional, nonlinear, or poorly modeled systems. A number of approaches have been applied to this problem, such as modeling inverse dynamics, backpropagating error through time, reinforcement learning, and dynamic programming based algorithms. The question of integrating parial a priori knowledge into these systems has often been a peripheral issue. Control systems for nonlinear plants have been explored extensively, especially approaches based on gain scheduling or adaptive control. Gain scheduling is the most commonly used, but requires extensive modeling and manual tuning, and doesn't work well with high-dimensional, nonlinear plants, or disturbances. Adaptive control addresses these problems, but usually can't react to spatial dependencies quickly enough to compete with a well-designed gain scheduled system. This thesis explores a hybrid control approach which uses a connectionist learning system to remember spatial nonlinearities discovered by an adaptive controller. The connectionist system learns to anticipate the parameters found by an indirect adaptive controller, effectively becoming a gain scheduled controller. The combined system is then able to exhibit some of the advantages of gain scheduled and adaptive control, without the extensive manual tuning required by traditional methods. A method is presented for making use of the partial derivative information from the network.