Open Loop Optimal Feedback Control for Continuous Linear Stochastic Systems


Book Description

The control of a continuous time linear system with parameters and disturbance represented by stochastic processes is studied. The optimal open loop control is shown to be a linear function of the expected value of the initial condition vector and the function specifying the control, the control generation matrix, is shown to be the solution to a Fredholm integral equation. A computational procedure is derived for the solution to the control generation matrix based on results by Kagiwada and Kalaba for the solution to a Fredholm integral equation. A closed loop control law, the open loop optimal feedback (OLOF) control, is derived from the optimal open loop control and the control generation matrix shown to be the solution to a Volterra integral equation. The OLOF CONTROL GENERATION MATRIX FOR THE TIME-INVARIANT, INFINITE TIME SYSTEM IS SHOWN TO BE A CONSTANT MATRIX. Some examples are worked to demonstrate the OLOF control and to compare it with the optimal open loop control. (Author).




Stochastic Optimal Control


Book Description

Presents techniques for optimizing problems in dynamic systems with terminal and path constraints. Includes optimal feedback control, feedback control for linear systems, and regulator synthesis. Offers iterative methods for solving nonlinear control problems. Demonstrates how to apply optimal control in a practical fashion. Serves as a text for graduate controls courses as offered in aerospace, mechanical and chemical engineering departments.




Linear Stochastic Control Systems


Book Description

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.




Stochastic Linear-Quadratic Optimal Control Theory: Open-Loop and Closed-Loop Solutions


Book Description

This book gathers the most essential results, including recent ones, on linear-quadratic optimal control problems, which represent an important aspect of stochastic control. It presents the results in the context of finite and infinite horizon problems, and discusses a number of new and interesting issues. Further, it precisely identifies, for the first time, the interconnections between three well-known, relevant issues – the existence of optimal controls, solvability of the optimality system, and solvability of the associated Riccati equation. Although the content is largely self-contained, readers should have a basic grasp of linear algebra, functional analysis and stochastic ordinary differential equations. The book is mainly intended for senior undergraduate and graduate students majoring in applied mathematics who are interested in stochastic control theory. However, it will also appeal to researchers in other related areas, such as engineering, management, finance/economics and the social sciences.




Optimal Control and Estimation


Book Description

"An excellent introduction to optimal control and estimation theory and its relationship with LQG design. . . . invaluable as a reference for those already familiar with the subject." — Automatica.This highly regarded graduate-level text provides a comprehensive introduction to optimal control theory for stochastic systems, emphasizing application of its basic concepts to real problems. The first two chapters introduce optimal control and review the mathematics of control and estimation. Chapter 3 addresses optimal control of systems that may be nonlinear and time-varying, but whose inputs and parameters are known without error.Chapter 4 of the book presents methods for estimating the dynamic states of a system that is driven by uncertain forces and is observed with random measurement error. Chapter 5 discusses the general problem of stochastic optimal control, and the concluding chapter covers linear time-invariant systems.Robert F. Stengel is Professor of Mechanical and Aerospace Engineering at Princeton University, where he directs the Topical Program on Robotics and Intelligent Systems and the Laboratory for Control and Automation. He was a principal designer of the Project Apollo Lunar Module control system."An excellent teaching book with many examples and worked problems which would be ideal for self-study or for use in the classroom. . . . The book also has a practical orientation and would be of considerable use to people applying these techniques in practice." — Short Book Reviews, Publication of the International Statistical Institute."An excellent book which guides the reader through most of the important concepts and techniques. . . . A useful book for students (and their teachers) and for those practicing engineers who require a comprehensive reference to the subject." — Library Reviews, The Royal Aeronautical Society.




Optimal Incomplete Feedback Control of Linear Stochastic Systems


Book Description

The problem of incomplete feedback control of stochastic linear systems is considered. The system is modeled by an uncertain parameter linear differential equation driven by Gaussian white noise and an incomplete observation which is a linear transformation of the states. The optimal control is the linear transformation which minimizes the expected value of a quadratic performance index. For both the finite and infinite time problems, necessary conditions that the optimal control law must satisfy are derived. Time varying and constant gains are considered for the finite time problem. For the infinite time problem only time invariant gains are considered. The gradient derived for the infinite time problem is applied to a flight control design problem. This problem concerns finding feedback gains to improve the lateral handling qualities of an F-4 at two different flight conditions. The resulting control laws give quite adequate aircraft handling qualities for the aircraft at both flight conditions.




Linear Optimal Control Systems


Book Description

"This book attempts to reconcile modern linear control theory with classical control theory. One of the major concerns of this text is to present design methods, employing modern techniques, for obtaining control systems that stand up to the requirements that have been so well developed in the classical expositions of control theory. Therefore, among other things, an entire chapter is devoted to a description of the analysis of control systems, mostly following the classical lines of thought. In the later chapters of the book, in which modern synthesis methods are developed, the chapter on analysis is recurrently referred to. Furthermore, special attention is paid to subjects that are standard in classical control theory but are frequently overlooked in modern treatments, such as nonzero set point control systems, tracking systems, and control systems that have to cope with constant disturbances. Also, heavy emphasis is placed upon the stochastic nature of control problems because the stochastic aspects are so essential." --Preface.




Stochastic Optimal Control in Infinite Dimension


Book Description

Providing an introduction to stochastic optimal control in infinite dimension, this book gives a complete account of the theory of second-order HJB equations in infinite-dimensional Hilbert spaces, focusing on its applicability to associated stochastic optimal control problems. It features a general introduction to optimal stochastic control, including basic results (e.g. the dynamic programming principle) with proofs, and provides examples of applications. A complete and up-to-date exposition of the existing theory of viscosity solutions and regular solutions of second-order HJB equations in Hilbert spaces is given, together with an extensive survey of other methods, with a full bibliography. In particular, Chapter 6, written by M. Fuhrman and G. Tessitore, surveys the theory of regular solutions of HJB equations arising in infinite-dimensional stochastic control, via BSDEs. The book is of interest to both pure and applied researchers working in the control theory of stochastic PDEs, and in PDEs in infinite dimension. Readers from other fields who want to learn the basic theory will also find it useful. The prerequisites are: standard functional analysis, the theory of semigroups of operators and its use in the study of PDEs, some knowledge of the dynamic programming approach to stochastic optimal control problems in finite dimension, and the basics of stochastic analysis and stochastic equations in infinite-dimensional spaces.




Stochastic Optimization and Control


Book Description

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