4 edition of Self-organizing control of stochastic systems found in the catalog.
Includes bibliographies and index.
|Statement||by George N. Saridis.|
|Series||Control and systems theory ; v. 4|
|LC Classifications||QA402.3 .S265|
|The Physical Object|
|Pagination||xxi, 488 p. :|
|Number of Pages||488|
|LC Control Number||75040645|
Techniques in Discrete-Time Stochastic Control Systems. Edited by C.T. Leondes. Vol Pages () Download full volume. Previous volume. Next volume. Actions for selected chapters. Book chapter Full text access Techniques for Reduced-Order Control of Stochastic Discrete-Time Weakly Coupled Large Scale Systems. Xuemin Shen, Zijad. In this thesis I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as : Carlos Gershenson.
Linear Stochastic Control Systems presents a thorough description of the mathematical theory and fundamental principles of linear stochastic control systems. Both continuous-time and discrete-time systems are thoroughly s of the modern. Self-organizing systems have forever produced integrated outcomes in nature (ecosystems) and in human societies (language). Perhaps the earliest examples of self-organizing commercial systems .
This book is concerned with robust control of stochastic systems. One of the main features is its coverage of jump Markovian systems. Overall, this book presents results taking into consideration both white noise and Markov chain perturbations. 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.
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Self-organizing control of stochastic systems (Control and systems theory ; v. 4) Hardcover – January 1, by George N Saridis (Author) › Visit Amazon's George N Saridis Page. Find all the books, read about the author, and more. See search results for Cited by: Additional Physical Format: Online version: Saridis, George N., Self-organizing control of stochastic systems.
New York: M. Dekker, © It employs a large number of examples to show how to build stochastic models of Self-organizing control of stochastic systems book systems, analyse these models to predict their performance, and use the analysis to design and control them.
The book provides a self-contained review of the relevant topics in probability theory: In discrete and continuous time Markov models it covers the transient and /5(4). We adopted the optimization method proposed by Mesquita et al.
[International workshop on hybrid systems: Computation and control, pp. ()] as a control method for the swarm robot systems. This method requires neither centralized controllers nor position identification of each robot, and we thus refer to it as “self-organizing control.”Author: Daisuke Inoue, Daisuke Murai, Hiroaki Yoshida.
In this context, the purpose of control system design becomes the selection of a control signal that makes the shape of the system output's p.d.f.
as close as possible to a given distribution. The book contains material on the subjects of: • Control of single-input single-output and multiple-input multiple-output stochastic by: Stochastic control, the control of random processes, has become increasingly more important to the systems analyst and engineer.
The Second IFAC Symposium on Stochastic Control represents current thinking on all aspects of stochastic control, both theoretical and practical, and as such represents a further advance in the understanding of such systems. A simple version of the problem of optimal control of stochastic systems is discussed, along with an example of an industrial application of this theory.
Subsequent discussions cover filtering and prediction theory as well as the general stochastic control problem for linear systems with quadratic by: Request PDF | Stochastic Self-organizing Control for Swarm Robot Systems | In swarm robot systems, forming a target shape with autonomously moving robots is an important task.
Considering cost and. DESIGN OF SELF-ORGANIZING CONTROL ALGORYTHMS Self-organizing systems under considera tion involve an interaction between con trol and learning processes (i.e. data accumulation). This interaction, based on the known control duality property in nonlinear stochastic systems (Feldbaum, ) gives rise to the main difficul ties in optimal control Cited by: 6.
Stochastic reversibility in self-organizing systems. Under a general self-organizing rule the book positions are rearranged when a borrowed book, originally in position i, is returned to the. This book will be a valuable resource for all practitioners, researchers, and professionals in applied mathematics and operations research who work in the areas of stochastic control, mathematical finance, queueing theory, and inventory systems.
It may also serve as a supplemental text for graduate courses in optimal control and its applications. The book provides a self-contained review of the relevant topics in probability theory. The rest of the book is devoted to important classes of stochastic models.
In discrete and continuous time Markov models it covers the transient and long term Brand: Springer-Verlag New York. In this book, a set of new approaches for the control of the output probability density function of stochastic dynamic systems (those subjected to any bounded random inputs), has been developed.
In this context, the purpose of control system design becomes the selection of a control signal that makes the shape Brand: Springer-Verlag London. Modeling, Analysis, Design, and Control of Stochastic Systems | V. Kulkarni. | download | B–OK.
Download books for free. Find books. Discrete-time Stochastic Systems gives a comprehensive introduction to the estimation and control of dynamic stochastic systems and provides complete derivations of key results such as the basic relations for Wiener filtering.
The book covers both state-space methods and those based on the polynomial by: 2. Saridis, G.N., Self-Organizing Control of Stochastic Systems, New York: Marcel Dekker, Translated under the title Samoorganizuyushchiesya stokhasticheskie Author: A.
Krasovskii, M. Misrikhanov. It employs a large number of examples to show how to build stochastic models of physical systems, analyse these models to predict their performance, and use the analysis to design and control them.
The book provides a self-contained review of the relevant topics in probability theory: In /5(3). Discrete-time Stochastic Systems gives a comprehensive introduction to the estimation and control of dynamic stochastic systems and provides complete derivations of key results such as the basic relations for Wiener filtering.
The book covers both state-space methods and those based on the polynomial approach. Similarities and differences between these approaches are Brand: Springer-Verlag London. Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system.
The system designer assumes, in a Bayesian probability-driven fashion, that random noise with known probability distribution affects the evolution and observation of the state. His research interests include sensor-based modeling and analysis of complex systems for process monitoring/control; system diagnostics/ prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self-organizing behaviors.
From the reviews: "The subject of the book is related to the development of a theory of linear stochastic systems including both white noise and jump Markov perturbations, and to the development of analysis and design methods for linear-quadratic control, robust stabilization and disturbance attenuation problems.The deterministic signals used for the design of control systems are often ‘proxies’ of real signals.
These proxies have simple shapes to reduce the design complexity and to allow for easy interpretation of the control system output. The resulting control systems are then optimal only for the chosen proxy signal and the applied : Rolf Isermann.This monograph presents a useful methodology for the control of such stochastic systems with a focus on robust stabilization in the mean square, linear quadratic control, the disturbance attenuation problem, and robust stabilization with respect to dynamic and parametric uncertainty.