Decentralized Control of Complex Systems

Decentralized Control of Complex Systems PDF

Author: Dragoslav D. Siljak

Publisher: Courier Corporation

Published: 2013-07-24

Total Pages: 546

ISBN-13: 0486294374

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Starting with a graph-theoretic framework for structural modeling of complex systems, this text presents results related to robust stabilization via decentralized state feedback. Subsequent chapters explore optimization, output feedback, the manipulative power of graphs, overlapping decompositions and the underlying inclusion principle, and reliability design. An appendix provides efficient graph algorithms. 1991 edition.

Control of Complex Systems

Control of Complex Systems PDF

Author: Aleksandar Zecevic

Publisher: Springer Science & Business Media

Published: 2010-01-08

Total Pages: 233

ISBN-13: 1441912169

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"Control of Complex Systems: Structural Constraints and Uncertainty" focuses on control design under information structure constraints, with a particular emphasis on large-scale systems. The complexity of such systems poses serious computational challenges and severely restricts the types of feedback laws that can be used in practice. This book systematically addresses the main issues, and provides a number of applications that illustrate potential design methods, most which use Linear Matrix Inequalities (LMIs), which have become a popular design tool over the past two decades. Authors Aleksandar I. Zecevic and Dragoslav D. Siljak use their years of experience in the control field to also: Address the issues of large-scale systems as they relate to robust control and linear matrix inequalities Discuss a new approach to applying standard LMI techniques to large-scale systems, combining graphic-theoretic decomposition techniques with appropriate low-rank numerical approximations and dramatically reducing the computational effort Providing numerous examples and a wide variety of applications, ranging from electric power systems and nonlinear circuits to mechanical problems and dynamic Boolean networks "Control of Complex Systems: Structural Constraints and Uncertainty" will appeal to practicing engineers, researchers and students working in control design and other related areas.

Nonlinear and Adaptive Control of Complex Systems

Nonlinear and Adaptive Control of Complex Systems PDF

Author: A.L. Fradkov

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 520

ISBN-13: 9401592616

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This volume presents a theoretical framework and control methodology for a class of complex dynamical systems characterised by high state space dimension, multiple inputs and outputs, significant nonlinearity, parametric uncertainty, and unmodeled dynamics. A unique feature of the authors' approach is the combination of rigorous concepts and methods of nonlinear control (invariant and attracting submanifolds, Lyapunov functions, exact linearisation, passification) with approximate decomposition results based on singular perturbations and decentralisation. Some results published previously in the Russian literature and not well known in the West are brought to light. Basic concepts of modern nonlinear control and motivating examples are given. Audience: This book will be useful for researchers, engineers, university lecturers and postgraduate students specialising in the fields of applied mathematics and engineering, such as automatic control, robotics, and control of vibrations.

Decentralized Estimation and Control for Multisensor Systems

Decentralized Estimation and Control for Multisensor Systems PDF

Author: Arthur G.O. Mutambara

Publisher: Routledge

Published: 2019-05-20

Total Pages: 252

ISBN-13: 1351456490

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Decentralized Estimation and Control for Multisensor Systems explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Such algorithms have extensive applications in modular robotics and complex or large scale systems, including the Mars Rover, the Mir station, and Space Shuttle Columbia. Most existing algorithms use some form of hierarchical or centralized structure for data gathering and processing. In contrast, in a fully decentralized system, all information is processed locally. A decentralized data fusion system includes a network of sensor nodes - each with its own processing facility, which together do not require any central processing or central communication facility. Only node-to-node communication and local system knowledge are permitted. Algorithms for decentralized data fusion systems based on the linear information filter have been developed, obtaining decentrally the same results as those in a conventional centralized data fusion system. However, these algorithms are limited, indicating that existing decentralized data fusion algorithms have limited scalability and are wasteful of communications and computation resources. Decentralized Estimation and Control for Multisensor Systems aims to remove current limitations in decentralized data fusion algorithms and to extend the decentralized principle to problems involving local control and actuation. The text discusses: Generalizing the linear Information filter to the problem of estimation for nonlinear systems Developing a decentralized form of the algorithm Solving the problem of fully connected topologies by using generalized model distribution where the nodal system involves only locally relevant states Reducing computational requirements by using smaller local model sizes Defining internodal communication Developing estima

Collectives and the Design of Complex Systems

Collectives and the Design of Complex Systems PDF

Author: Kagan Tumer

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 329

ISBN-13: 1441989099

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Many complex systems found in nature can be viewed as function optimizers. In particular, they can be viewed as such optimizers of functions in extremely high dimensional spaces. Given the difficulty of performing such high-dimensional op timization with modern computers, there has been a lot of exploration of computa tional algorithms that try to emulate those naturally-occurring function optimizers. Examples include simulated annealing (SA [15,18]), genetic algorithms (GAs) and evolutionary computation [2,3,9,11,20-22,24,28]. The ultimate goal of this work is an algorithm that can, for any provided high-dimensional function, come close to extremizing that function. Particularly desirable would be such an algorithm that works in an adaptive and robust manner, without any explicit knowledge of the form of the function being optimized. In particular, such an algorithm could be used for distributed adaptive control---one of the most important tasks engineers will face in the future, when the systems they design will be massively distributed and horribly messy congeries ofcomputational systems.

Decentralized Control and Filtering in Interconnected Dynamical Systems

Decentralized Control and Filtering in Interconnected Dynamical Systems PDF

Author: Magdi S. Mahmoud

Publisher: CRC Press

Published: 2010-11-23

Total Pages: 610

ISBN-13: 1439838178

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Based on the many approaches available for dealing with large-scale systems (LSS), Decentralized Control and Filtering in Interconnected Dynamical Systems supplies a rigorous framework for studying the analysis, stability, and control problems of LSS. Providing an overall assessment of LSS theories, it addresses model order reduction, parametric un

Directions in Large-Scale Systems

Directions in Large-Scale Systems PDF

Author: Y. Ho

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 430

ISBN-13: 1468422596

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This book is the record of papers presented at the Conference on Directions in Decentralized Control, Many-Person Optimization, and Large-Scale Systems held at the Colonial Hilton Inn, Wakefield, Massachusetts from September 1-3, 1975. Our motivation for organizing such a conference was two fold. Firstly, the last few years have seen a great deal of activity in the field of Large-Scale Systems Theory and it has been certainly one of the dominant themes of research in the disciplines of Systems and Control Theory. It therefore seemed appropriate to try and take stock of what had been accomplished and also try to "invent"l the future directions of research in this field. Secondly, the 6th World IFAC Conference was being held in Cambridge, Massachusetts the week earlier and it provided an ideal opportunity for taking advantage of the presence of a large number of specialists from all parts of the world to organize a small conference where a free exchange of ideas could take place. It is left to the readers of this volume to judge to what extent we have been successful in our above mentioned goals. There is no accepted definition of what constitutes a "large scale system" nor what large-scale system theory is. While this diversity does suggest that the field {whatever it may turn out to be} is in a state of flux, it does not necessarily imply chaos.

Decentralized Control of Large-Scale Systems

Decentralized Control of Large-Scale Systems PDF

Author: Edward J. Davison

Publisher: Springer

Published: 2019-09-25

Total Pages: 164

ISBN-13: 9781441960139

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A large-scale system is composed of several interconnected subsystems. For such a system it is often desired to have some form of decentralization in the control structure, since it is typically not realistic to assume that all output measurements can be transmitted to every local control station. Problems of this kind can appear in electric power systems, communication networks, large space structures, robotic systems, economic systems, and traffic networks, to name only a few. Typical large-scale control systems have several local control stations which observe only local outputs and control only local inputs. All controllers are involved, however, in the control operation of the overall system. The focus of this book is on the efficient control of interconnected systems, and it presents systems analysis and controller synthesis techniques using a variety of methods. A systematic study of multi-input, multi-output systems is carried out and illustrative examples are given to clarify the ideas.

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