Deterministic and Stochastic Optimal Control

by Wendell H. Fleming, Raymond W. Rishel

Overview

This book may be regarded as consisting of two parts. In Chapters I-IV we pre­ sent what we regard as essential topics in an introduction to deterministic optimal control theory. This material has been used by the authors for one semester graduate-level courses at Brown University and the University of Kentucky. The simplest problem in calculus of variations is taken as the point of departure, in Chapter I. Chapters II, III, and IV deal with necessary conditions for an opti­ mum, existence and regularity theorems for optimal controls, and the method of dynamic programming. The beginning reader may find it useful first to learn the main results, corollaries, and examples. These tend to be found in the earlier parts of each chapter. We have deliberately postponed some difficult technical proofs to later parts of these chapters. In the second part of the book we give an introduction to shastic optimal control for Markov diffusion processes. Our treatment follows the dynamic pro­ gramming method, and depends on the intimate relationship between second­ order partial differential equations of parabolic type and shastic differential equations. This relationship is reviewed in Chapter V, which may be read inde­ pendently of Chapters I-IV. Chapter VI is based to a considerable extent on the authors' work in shastic control since 1961. It also includes two other topics important for applications, namely, the solution to the shastic linear regulator and the separation principle.

Product Details

ISBN-13:	9780387901558
Publisher:	Springer New York
Publication date:	11/17/1975
Series:	Stochastic Modelling and Applied Probability , #1
Edition description:	1975
Pages:	222
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

I The Simplest Problem in Calculus of Variations.- 1. Introduction.- 2. Minimum Problems on an Abstract Space—Elementary Theory.- 3. The Euler Equation; Extremals.- 4. Examples.- 5. The Jacobi Necessary Condition.- 6. The Simplest Problem in n Dimensions.- II The Optimal Control Problem.- 1. Introduction.- 2. Examples.- 3. Statement of the Optimal Control Problem.- 4. Equivalent Problems.- 5. Statement of Pontryagin’s Principle.- 6. Extremals for the Moon Landing Problem.- 7. Extremals for the Linear Regulator Problem.- 8. Extremals for the Simplest Problem in Calculus of Variations.- 9. General Features of the Moon Landing Problem.- 10. Summary of Preliminary Results.- 11. The Free Terminal Point Problem.- 12. Preliminary Discussion of the Proof of Pontryagin’s Principle.- 13. A Multiplier Rule for an Abstract Nonlinear Programming Problem.- 14. A Cone of Variations for the Problem of Optimal Control.- 15. Verification of Pontryagin’s Principle.- III Existence and Continuity Properties of Optimal Controls.- 1. The Existence Problem.- 2. An Existence Theorem (Mayer Problem U Compact).- 3. Proof of Theorem 2.1.- 4. More Existence Theorems.- 5. Proof of Theorem 4.1.- 6. Continuity Properties of Optimal Controls.- IV Dynamic Programming.- 1. Introduction.- 2. The Problem.- 3. The Value Function.- 4. The Partial Differential Equation of Dynamic Programming.- 5. The Linear Regulator Problem.- 6. Equations of Motion with Discontinuous Feedback Controls.- 7. Sufficient Conditions for Optimality.- 8. The Relationship between the Equation of Dynamic Programming and Pontryagin’s Principle.- V Shastic Differential Equations and Markov Diffusion Processes.- 1. Introduction.- 2. Continuous Shastic Processes; Brownian Motion Processes.- 3. Ito’s ShasticIntegral.- 4. Shastic Differential Equations.- 5. Markov Diffusion Processes.- 6. Backward Equations.- 7. Boundary Value Problems.- 8. Forward Equations.- 9. Linear System Equations; the Kalman-Bucy Filter.- 10. Absolutely Continuous Substitution of Probability Measures.- 11. An Extension of Theorems 5.1,5.2.- VI Optimal Control of Markov Diffusion Processes.- 1. Introduction.- 2. The Dynamic Programming Equation for Controlled Markov Processes.- 3. Controlled Diffusion Processes.- 4. The Dynamic Programming Equation for Controlled Diffusions; a Verification Theorem.- 5. The Linear Regulator Problem (Complete Observations of System States).- 6. Existence Theorems.- 7. Dependence of Optimal Performance on y and—.- 8. Generalized Solutions of the Dynamic Programming Equation.- 9. Shastic Approximation to the Deterministic Control Problem.- 10. Problems with Partial Observations.- 11. The Separation Principle.- Appendices.- A. Gronwall-Bellman Inequality.- B. Selecting a Measurable Function.- C. Convex Sets and Convex Functions.- D. Review of Basic Probability.- E. Results about Parabolic Equations.- F. A General Position Lemma.