Decentralized Control of Complex Systems
| Author | : S?iljak |
| Publisher | : Academic Press |
| Total Pages | : 543 |
| Release | : 1991-01-28 |
| Genre | : Computers |
| ISBN | : 0080958710 |
Decentralized Control of Complex Systems
Decentralized Control Of Complex Systems
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Decentralized Control of Complex Systems
| Author | : Dragoslav D. Siljak |
| Publisher | : Courier Corporation |
| Total Pages | : 546 |
| Release | : 2013-07-24 |
| Genre | : Technology & Engineering |
| ISBN | : 0486294374 |
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.
Decentralized Control and Filtering in Interconnected Dynamical Systems
| Author | : Magdi S. Mahmoud |
| Publisher | : CRC Press |
| Total Pages | : 610 |
| Release | : 2010-11-23 |
| Genre | : Business & Economics |
| ISBN | : 1439838178 |
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
Decentralized Estimation and Control for Multisensor Systems
| Author | : Arthur G.O. Mutambara |
| Publisher | : Routledge |
| Total Pages | : 252 |
| Release | : 2019-05-20 |
| Genre | : Technology & Engineering |
| ISBN | : 1351456490 |
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
| Author | : Kagan Tumer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 329 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1441989099 |
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.
Control of Complex Systems
| Author | : Aleksandar Zecevic |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 233 |
| Release | : 2010-01-08 |
| Genre | : Science |
| ISBN | : 1441912169 |
"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.
Control of Complex Systems
| Author | : Kyriakos Vamvoudakis |
| Publisher | : Butterworth-Heinemann |
| Total Pages | : 764 |
| Release | : 2016-07-27 |
| Genre | : Technology & Engineering |
| ISBN | : 0128054379 |
In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: "Introduction and Background on Control Theory, "Adaptive Control and Neuroscience, "Adaptive Learning Algorithms, "Cyber-Physical Systems and Cooperative Control, "Applications.The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists. This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems. • Collection of chapters from several well-known professors and researchers that will showcase their recent work • Presents different state-of-the-art control approaches and theory for complex systems • Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams • Real system examples and figures throughout, make ideas concrete - Includes chapters from several well-known professors and researchers that showcases their recent work - Presents different state-of-the-art control approaches and theory for complex systems - Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams - Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems
Variable Structure Control of Complex Systems
| Author | : Xing-Gang Yan |
| Publisher | : Springer |
| Total Pages | : 363 |
| Release | : 2016-12-05 |
| Genre | : Technology & Engineering |
| ISBN | : 3319489623 |
This book systematizes recent research work on variable-structure control. It is self-contained, presenting necessary mathematical preliminaries so that the theoretical developments can be easily understood by a broad readership. The text begins with an introduction to the fundamental ideas of variable-structure control pertinent to their application in complex nonlinear systems. In the core of the book, the authors lay out an approach, suitable for a large class of systems, that deals with system uncertainties with nonlinear bounds. Its treatment of complex systems in which limited measurement information is available makes the results developed convenient to implement. Various case-study applications are described, from aerospace, through power systems to river pollution control with supporting simulations to aid the transition from mathematical theory to engineering practicalities. The book addresses systems with nonlinearities, time delays and interconnections and considers issues such as stabilization, observer design, and fault detection and isolation. It makes extensive use of numerical and practical examples to render its ideas more readily absorbed. Variable-Structure Control of Complex Systems will be of interest to academic researchers studying control theory and its application in nonlinear, time-delayed an modular large-scale systems; the robustness of its approach will also be attractive to control engineers working in industries associate with aerospace, electrical and mechanical engineering.
Nonlinear and Adaptive Control of Complex Systems
| Author | : A.L. Fradkov |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 520 |
| Release | : 2013-06-29 |
| Genre | : Science |
| ISBN | : 9401592616 |
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.
Control System Design
| Author | : Bernard Friedland |
| Publisher | : Courier Corporation |
| Total Pages | : 530 |
| Release | : 2012-03-08 |
| Genre | : Science |
| ISBN | : 048613511X |
Introduction to state-space methods covers feedback control; state-space representation of dynamic systems and dynamics of linear systems; frequency-domain analysis; controllability and observability; shaping the dynamic response; more. 1986 edition.
