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Deterministic control of uncertain systems

  • 362 Pages
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  • English

P. Peregrinus on behalf of the Institution of Electrical Engineers , London, U.K
Automatic control., Control th
Statementedited by A.S.I. Zinober.
SeriesIEE control engineering series ;, 40
ContributionsZinober, A. S. I.
Classifications
LC ClassificationsTJ213 .D47 1990
The Physical Object
Paginationxiv, 362 p. ;
ID Numbers
Open LibraryOL1917557M
ISBN 100863411703
LC Control Number90127082

Get this from a library. Deterministic control of uncertain systems. [A S I Zinober;] -- This volume covers both the historical and current state of variable structure control, with theory illustrated by practical examples.

Among the topics examined are switching command devices. In book: Deterministic Control of Uncertain Systems, Chapter: 11, Publisher: Peregrinus London, Editors: r, pp The authors consider the deterministic control problem for a.

Includes sections on: Sliding mode control with switching command devices. Hyperplane design and CAD of variable structure control systems.

Variable structure controllers for robots. The hyperstability approach to VSCS design. Nonlinear continuous feedback for robust tracking. Control of uncertain systems with neglected dynamics.

Deterministic Control of Uncertain Systems Edited by A.S.I. Zinober One of the main fields of study in the control of dynamical systems has been the effective control of time-varying systems with uncertain parameters and external disturbances.

The deterministic control of uncertain time-varying systems control is achieved using nonlinear feedback control functions, which operate effectively over a specified magnitude range of a class of system parameter variations, without the need for.

One of the main fields of study in the control of dynamical systems has been the effective control of Deterministic control of uncertain systems book systems with uncertain parameters and external disturbances.

In contrast to stochastic adaptive controllers with identification algorithms, the deterministic control of uncertain time-varying systems has a fixed nonlinear feedback controller, which operates effectively.

Uncertain systems, deterministic control, guaranteed stability, feedback control, adaptive control. INTRODUCTION In order to predict or control the behavior of a system in the "real" world, be it phys ical, biological or socio-economic, the system analyst seeks to capture its salient features in an abstraction, a mathematical by: A Lyapunov theory approachChapter Control of uncertain systems with neglected dynamicsChapter Nonlinear composite control of a class of nominally linear singularly perturbed uncertain systemsChapter Some extensions of variable structure control theory for the control of nonlinear systemsChapter Continuous self-adaptive control.

Cite this paper as: Corless M., Leitmann G. () Deterministic control of uncertain systems. In: Byrnes C.I., Kurzhanski A.B. (eds) Modelling and Adaptive by: “This book is a concise, clear and well-organized masterpiece on robust control theory for linear systems with parameter-uncertainty.

as a textbook for understanding the basic concepts and state-space methodologies about robust control, this book is one of the best ones I have ever read.” (Yun Zou, Mathematical Reviews, November, )Cited by:,Deterministic Control of Uncertain Systems, A.S.I. Zinober, buy best price Deterministic Control of Uncertain.

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain by: Leitmann G.

() Deterministic control of uncertain systems via a constructive use of Lyapunov stability theory. In: Sebastian H.J., Tammer K. (eds) System Modelling and Optimization. Lecture Notes in Control and Information Sciences, vol Cited by: It provides systematic design approaches for identification, recognition, and control of linear uncertain systems.

Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.

The presence of uncertainty in a system description has always been a critical issue in control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain Systems, with Applications (Second Edition) is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of systems subject to deterministic and stochastic.

Robust controllers for nonlinear systems with uncertain parameters can be reliably designed using probabilistic methods.

In this chapter, a design approach based on the combination of stochastic. Sergio J. Rey, in International Encyclopedia of the Social & Behavioral Sciences (Second Edition), Deterministic versus Stochastic Models. A deterministic model is one in which the values for the dependent variables of the system are completely determined by the parameters of the model.

In contrast, stochastic, or probabilistic, models introduce randomness in such a way that the. This chapter considers the problem of obtaining memoryless stabilising feedback controllers for uncertain dynamical systems described by ordinary differential equations.

Various classes of controllers are presented. The design of all these controllers is based on Lyapunov theory. The results to obtain tracking controllers for a general class of uncertain mechanical systems was Cited by: "The book is structured into two parts [Part I: Deterministic Control; Part II: Stochastic Control].

The Introduction presents some dynamical time-delay systems and gives the notations used in the book, states the problems and the difference between the two systems under discussion.

Different design algorithms for state feedback are proposed. In physics. Physical laws that are described by differential equations represent deterministic systems, even though the state of the system at a given point in time may be difficult to describe explicitly.

In quantum mechanics, the Schrödinger equation, which describes the continuous time evolution of a system's wave function, is r, the relationship between a system's. Randomized Algorithms for Analysis and Control of Uncertain Systems ISBN Engineering – Monograph (English) J Springer-Verlag Berlin Heidelberg NewYork London Paris Tokyo nity, it is an appropriate time for a book such as this.

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The central theme of. Adaptive Identification and Control of Uncertain Systems with Nonsmooth Dynamics reports some of the latest research on modeling, identification and adaptive control for systems with nonsmooth dynamics (e.g., backlash, dead zone, friction, saturation, etc). The authors present recent research results for the modelling and control designs of.

The material presented in this book addresses the analysis and design of learning control systems. It begins with an introduction to the concept of learning control, including a comprehensive literature review.

The text follows with a complete and unifying analysis of the learning control Author: Kevin L.

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Moore. Provides connections between lyapunov-based matrix approach and the transfer function based polynomial approaches. Robust Control of Uncertain Dynamic Systems: A Linear State Space Approach is an ideal book for first year graduate students taking a course in robust control in aerospace, mechanical, or electrical engineering.

Englisch. Book Description. Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments.

It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. The success of the distributed randomized methods proposed by Roberto Tempo is witnessed by the monograph “Randomized Algorithms for Analysis and Control of Uncertain Systems”, that was published by Springer-Verlag, and that represents a pioneering textbook, lying the foundations of probabilistic methods in the analysis and design of Alma mater: Politecnico di Torino.

Deterministic Control of Uncertain Systems A.S.I. Zinober This book is a comprehensive collection of the latest in control theories that lay a solid foundation for the establishment of glocal control theory for the coming decades.

In stock. Limited Quantity: 5 in stock. Free Online Library: Deterministic learning theory for identification, recognition, and control.(Brief article, Book review) by "SciTech Book News"; Publishing industry Library and information science Science and technology, general Books Book reviews Control systems.

This book directly addresses these issues from a deterministic uncertainty viewpoint and focuses on the interval parameter characterization of uncertain systems. Various tools of analysis and design are presented in a consolidated manner. Deterministic sampling (DS) of uncertain systems is a viable alternative to random sampling (RS).

Instead of using random generators, specific DS rules are devised to generate appropriate, but still statistical (Fermi’s notation, see section ) by: 6.

Deterministic Networking (DetNet) is an effort by the IETF DetNet Working Group to study implementation of deterministic data paths for real-time applications with extremely low data loss rates, packet delay variation (jitter), and bounded latency, such as audio and video streaming, industrial automation, and vehicle control.

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DetNet operates at the IP Layer 3 routed segments. Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments.

It provides systematic design approaches for identification, recognition, and control of linear uncertain systems.Randomized Algorithms for Analysis and Control of Uncertain Systems (second edition) is certain to interest academic researchers and graduate control students working in probabilistic, robust or optimal control methods and control engineers dealing with system uncertainties.

The present book is a very timely contribution to the literature.