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Book Control of Stochastic Linear Discrete Systems with Random Parameters

Download or read book Control of Stochastic Linear Discrete Systems with Random Parameters written by Robert Richard Valleni and published by . This book was released on 1969 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Linear Stochastic Control Systems

Download or read book Linear Stochastic Control Systems written by Goong Chen and published by CRC Press. This book was released on 1995-07-12 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 covered. Reviews of the modern probability and random processes theories and the Itô stochastic differential equations are provided. Discrete-time stochastic systems theory, optimal estimation and Kalman filtering, and optimal stochastic control theory are studied in detail. A modern treatment of these same topics for continuous-time stochastic control systems is included. The text is written in an easy-to-understand style, and the reader needs only to have a background of elementary real analysis and linear deterministic systems theory to comprehend the subject matter. This graduate textbook is also suitable for self-study, professional training, and as a handy research reference. Linear Stochastic Control Systems is self-contained and provides a step-by-step development of the theory, with many illustrative examples, exercises, and engineering applications.

Book Discrete time Stochastic Systems

Download or read book Discrete time Stochastic Systems written by Torsten Söderström and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive introduction to the estimation and control of dynamic stochastic systems provides complete derivations of key results. The second edition includes improved and updated material, and a new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.

Book Linear Stochastic Systems

Download or read book Linear Stochastic Systems written by Peter E. Caines and published by SIAM. This book was released on 2018-06-12 with total page 892 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear Stochastic Systems, originally published in 1988, is today as comprehensive a reference to the theory of linear discrete-time-parameter systems as ever. Its most outstanding feature is the unified presentation, including both input-output and state space representations of stochastic linear systems, together with their interrelationships. The author first covers the foundations of linear stochastic systems and then continues through to more sophisticated topics including the fundamentals of stochastic processes and the construction of stochastic systems; an integrated exposition of the theories of prediction, realization (modeling), parameter estimation, and control; and a presentation of stochastic adaptive control theory. Written in a clear, concise manner and accessible to graduate students, researchers, and teachers, this classic volume also includes background material to make it self-contained and has complete proofs for all the principal results of the book. Furthermore, this edition includes many corrections of errata collected over the years.

Book Optimization of Stochastic Systems

Download or read book Optimization of Stochastic Systems written by Masanao Aoki and published by Elsevier. This book was released on 2016-06-03 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization of Stochastic Systems

Book Linear Systems Control

Download or read book Linear Systems Control written by Elbert Hendricks and published by Springer Science & Business Media. This book was released on 2008-10-13 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern control theory and in particular state space or state variable methods can be adapted to the description of many different systems because it depends strongly on physical modeling and physical intuition. The laws of physics are in the form of differential equations and for this reason, this book concentrates on system descriptions in this form. This means coupled systems of linear or nonlinear differential equations. The physical approach is emphasized in this book because it is most natural for complex systems. It also makes what would ordinarily be a difficult mathematical subject into one which can straightforwardly be understood intuitively and which deals with concepts which engineering and science students are already familiar. In this way it is easy to immediately apply the theory to the understanding and control of ordinary systems. Application engineers, working in industry, will also find this book interesting and useful for this reason. In line with the approach set forth above, the book first deals with the modeling of systems in state space form. Both transfer function and differential equation modeling methods are treated with many examples. Linearization is treated and explained first for very simple nonlinear systems and then more complex systems. Because computer control is so fundamental to modern applications, discrete time modeling of systems as difference equations is introduced immediately after the more intuitive differential equation models. The conversion of differential equation models to difference equations is also discussed at length, including transfer function formulations. A vital problem in modern control is how to treat noise in control systems. Nevertheless this question is rarely treated in many control system textbooks because it is considered to be too mathematical and too difficult in a second course on controls. In this textbook a simple physical approach is made to the description of noise and stochastic disturbances which is easy to understand and apply to common systems. This requires only a few fundamental statistical concepts which are given in a simple introduction which lead naturally to the fundamental noise propagation equation for dynamic systems, the Lyapunov equation. This equation is given and exemplified both in its continuous and discrete time versions. With the Lyapunov equation available to describe state noise propagation, it is a very small step to add the effect of measurements and measurement noise. This gives immediately the Riccati equation for optimal state estimators or Kalman filters. These important observers are derived and illustrated using simulations in terms which make them easy to understand and easy to apply to real systems. The use of LQR regulators with Kalman filters give LQG (Linear Quadratic Gaussian) regulators which are introduced at the end of the book. Another important subject which is introduced is the use of Kalman filters as parameter estimations for unknown parameters. The textbook is divided into 7 chapters, 5 appendices, a table of contents, a table of examples, extensive index and extensive list of references. Each chapter is provided with a summary of the main points covered and a set of problems relevant to the material in that chapter. Moreover each of the more advanced chapters (3 - 7) are provided with notes describing the history of the mathematical and technical problems which lead to the control theory presented in that chapter. Continuous time methods are the main focus in the book because these provide the most direct connection to physics. This physical foundation allows a logical presentation and gives a good intuitive feel for control system construction. Nevertheless strong attention is also given to discrete time systems. Very few proofs are included in the book but most of the important results are derived. This method of presentation makes the text very readable and gives a good foundation for reading more rigorous texts. A complete set of solutions is available for all of the problems in the text. In addition a set of longer exercises is available for use as Matlab/Simulink ‘laboratory exercises’ in connection with lectures. There is material of this kind for 12 such exercises and each exercise requires about 3 hours for its solution. Full written solutions of all these exercises are available.

Book Control and System Theory of Discrete Time Stochastic Systems

Download or read book Control and System Theory of Discrete Time Stochastic Systems written by Jan H. van Schuppen and published by Springer Nature. This book was released on 2021-08-02 with total page 940 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps students, researchers, and practicing engineers to understand the theoretical framework of control and system theory for discrete-time stochastic systems so that they can then apply its principles to their own stochastic control systems and to the solution of control, filtering, and realization problems for such systems. Applications of the theory in the book include the control of ships, shock absorbers, traffic and communications networks, and power systems with fluctuating power flows. The focus of the book is a stochastic control system defined for a spectrum of probability distributions including Bernoulli, finite, Poisson, beta, gamma, and Gaussian distributions. The concepts of observability and controllability of a stochastic control system are defined and characterized. Each output process considered is, with respect to conditions, represented by a stochastic system called a stochastic realization. The existence of a control law is related to stochastic controllability while the existence of a filter system is related to stochastic observability. Stochastic control with partial observations is based on the existence of a stochastic realization of the filtration of the observed process.​

Book Adaptive Stochastic Control for Linear Systems Part I Solution Method

Download or read book Adaptive Stochastic Control for Linear Systems Part I Solution Method written by Edison Tse and published by . This book was released on 1970 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem considered in this two-part paper deals with the control of linear, discrete-time, stochastic systems with unknown (possibly time-varying and random) gain parameters. The philosophy of control is based on the use of an open-loop-feedback-optimal (O.L.F.O.) control using a quadratic index of performance. In Part I it is shown that the O.L.F.O. system consists of (1) an identifier that estimates the system state variables and gain parameters, and (2) by a controller described by an 'adaptive' gain and correction term. Several qualitative properties of the overall system are obtained from an interpretation of the equations. Part II deals with the asymptotic properties of the O.L.F.O. adaptive system and with simulation results dealing with the control of stable and unstable third order plants. Comparisons are carried out with the optimal system when the parameters are known. In addition, the simulation results are interpreted in the context of the qualitative conclusions reached in Part I. (Author).

Book Adaptive Control of Stochastic Linear Systems with Unknown Parameters

Download or read book Adaptive Control of Stochastic Linear Systems with Unknown Parameters written by Richard Tse-Min Ku and published by . This book was released on 1972 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis considers the problem of optimal control of linear discrete-time stochastic dynamical system with unknown and, possibly, stochastically varying parameters on the basis of noisy measurements. It is desired to minimize the expected value of a quadratic cost functional. Since the simultaneous estimation of the state and plant parameters is a nonlinear filtering problem, the extended Kalman filter algorithm is used. The open-loop feedback optimal control technique is investigated as a computationally feasible solution to the adaptive stochastic control problem. The open-loop feedback optimal control system adaptive gains depend on the current and future uncertainty of the parameters estimation. Thus, the standard Separation Theorem does not hold in this problem. Suboptimal control system in which Separation Theorem is arbitrarily enforced is also considered. (Author).

Book Stochastic H2 H Control A Nash Game Approach

Download or read book Stochastic H2 H Control A Nash Game Approach written by Weihai Zhang and published by CRC Press. This book was released on 2017-08-07 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: The H∞ control has been one of the important robust control approaches since the 1980s. This book extends the area to nonlinear stochastic H2/H∞ control, and studies more complex and practically useful mixed H2/H∞ controller synthesis rather than the pure H∞ control. Different from the commonly used convex optimization method, this book applies the Nash game approach to give necessary and sufficient conditions for the existence and uniqueness of the mixed H2/H∞ control. Researchers will benefit from our detailed exposition of the stochastic mixed H2/H∞ control theory, while practitioners can apply our efficient algorithms to address their practical problems.

Book Stochastic Models Estimation and Control

Download or read book Stochastic Models Estimation and Control written by Peter S. Maybeck and published by Academic Press. This book was released on 1982-08-25 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.

Book Control and Dynamic Systems V28

Download or read book Control and Dynamic Systems V28 written by C.T. Leonides and published by Elsevier. This book was released on 2012-12-02 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control and Dynamic Systems: Advances in Theory in Applications, Volume 28: Advances in Algorithms and Computational Techniques in Dynamic Systems Control, Part 1 of 3 discusses developments in algorithms and computational techniques for control and dynamic systems. This book presents algorithms and numerical techniques used for the analysis and control design of stochastic linear systems with multiplicative and additive noise. It also discusses computational techniques for the matrix pseudoinverse in minimum variance reduced-order filtering and control; decomposition technique in multiobjective discrete-time dynamic problems; computational techniques in robotic systems; reduced complexity algorithm using microprocessors; algorithms for image-based tracking; and modeling of linear and nonlinear systems. This volume will be an important reference source for practitioners in the field who are looking for techniques with significant applied implications.

Book Adaptive Stochastic Control for Linear Systems Part Ii Asymptotic Properties and Simulation Results

Download or read book Adaptive Stochastic Control for Linear Systems Part Ii Asymptotic Properties and Simulation Results written by Michael Athans and published by . This book was released on 1970 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem considered in this two-part paper deals with the control of linear, discrete-time, stochastic systems with unknown (possibly time-varying and random) gain parameters. The philosophy of control is based on the use of an open-loop-feedback-optimal (O.L.F.O.) control using a quadratic index of performance. in Part I it is shown that the O.L.F.O. system consists of (1) an identifier that estimates the system state variables and gain parameters, and (2) by a controller described by an 'adaptive' gain and correction term. Several qualitative properties of the overal system are obtained from an interpretation of the equations. Part II deals with the asymptotic properties of the O.L.F.O. adaptive system and with simulation results dealing with the control of stable and unstable third order plants. Comparisons are carried out with the optimal system when the parameters are known. In addition, the simulation results are interpreted in the context of the qualitative conclusions reached in Part I. (Author).

Book Optimal Control Methods for Linear Discrete Time Economic Systems

Download or read book Optimal Control Methods for Linear Discrete Time Economic Systems written by Y. Murata and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: As our title reveals, we focus on optimal control methods and applications relevant to linear dynamic economic systems in discrete-time variables. We deal only with discrete cases simply because economic data are available in discrete forms, hence realistic economic policies should be established in discrete-time structures. Though many books have been written on optimal control in engineering, we see few on discrete-type optimal control. More over, since economic models take slightly different forms than do engineer ing ones, we need a comprehensive, self-contained treatment of linear optimal control applicable to discrete-time economic systems. The present work is intended to fill this need from the standpoint of contemporary macroeconomic stabilization. The work is organized as follows. In Chapter 1 we demonstrate instru ment instability in an economic stabilization problem and thereby establish the motivation for our departure into the optimal control world. Chapter 2 provides fundamental concepts and propositions for controlling linear deterministic discrete-time systems, together with some economic applica tions and numerical methods. Our optimal control rules are in the form of feedback from known state variables of the preceding period. When state variables are not observable or are accessible only with observation errors, we must obtain appropriate proxies for these variables, which are called "observers" in deterministic cases or "filters" in stochastic circumstances. In Chapters 3 and 4, respectively, Luenberger observers and Kalman filters are discussed, developed, and applied in various directions. Noticing that a separation principle lies between observer (or filter) and controller (cf.