This paper gives a survey of the various forms of Pontryagin's maximum principle for optimal control problems with state variable inequality constraints. The relations between the different sets of optimality conditions arising in these forms are shown. Furthermore, the application of these maximum principle conditions is demonstrated by solving some illustrative examples.
While optimality conditions for optimal control problems with state constraints have been extensively investigated in the literature the results pertaining to numerical methods are relatively scarce. This book fills the gap by providing a family of new methods. Among others, a novel convergence analysis of optimal control algorithms is introduced. The analysis refers to the topology of relaxed controls only to a limited degree and makes little use of Lagrange multipliers corresponding to state constraints. This approach enables the author to provide global convergence analysis of first order and superlinearly convergent second order methods. Further, the implementation aspects of the methods developed in the book are presented and discussed. The results concerning ordinary differential equations are then extended to control problems described by differential-algebraic equations in a comprehensive way for the first time in the literature.
This thesis deals with the investigation of geometric aspects of optimal control problems with state inequality constraints. An 'unrestricted' maximum principle is derived, whose associated adjoint equation possesses a solution which is continuous, except under special circumstances, even at junction points of an optimal trajectory with the state boundary. This result is shown to be valid under the assumption of regularity (in the sense of Pontryagin) as well as for certain non-regular problems. The relation between the 'unrestricted' maximum principle and the restricted one of Pontryagin is demonstrated. This investigation is based on the geometric notions introduced by Blaquiere and Leitmann and constitutes an extension of their work to problems with state variable inequality constraints. This geometric approach is contrasted with the approach of Dynamic Programming. (Author).
This book introduces a student to Pontryagin's "Maximum" Principle in a tutorial style. How to formulate an optimal control problem and how to apply Pontryagin's theory are the main topics. Numerous examples are used to discuss pitfalls in problem formulation. Figures are used extensively to complement the ideas. An entire chapter is dedicated to solved example problems: from the classical Brachistochrone problem to modern space vehicle guidance. These examples are also used to show how to obtain optimal nonlinear feedback control. Students in engineering and mathematics will find this book to be a useful complement to their lecture notes. Table of Contents: 1 Problem Formulation 1.1 The Brachistochrone Paradigm 1.1.1 Development of a Problem Formulation 1.1.2 Scaling Equations 1.1.3 Alternative Problem Formulations 1.1.4 The Target Set 1.2 A Fundamental Control Problem 1.2.1 Problem Statement 1.2.2 Trajectory Optimization and Feedback Control 2 Pontryagin's Principle 2.1 A Fundamental Control Problem 2.2 Necessary Conditions 2.3 Minimizing the Hamiltonian 2.3.1 Brief History 2.3.2 KKT Conditions for Problem HMC 2.3.3 Time-Varying Control Space 3 Example Problems 3.1 The Brachistochrone Problem Redux 3.2 A Linear-Quadratic Problem 3.3 A Time-Optimal Control Problem 3.4 A Space Guidance Problem 4 Exercise Problems 4.1 One-Dimensional Problems 4.1.1 Linear-Quadratic Problems 4.1.2 A Control-Constrained Problem 4.2 Double Integrator Problems 4.2.1 L1-Optimal Control 4.2.2 Fuller's Problem 4.3 Orbital Maneuvering Problems 4.3.1 Velocity Steering 4.3.2 Max-Energy Orbit Transfer 4.3.3 Min-Time Orbit Transfer References Index
The aim of this book is to furnish the reader with a rigorous and detailed exposition of the concept of control parametrization and time scaling transformation. It presents computational solution techniques for a special class of constrained optimal control problems as well as applications to some practical examples. The book may be considered an extension of the 1991 monograph A Unified Computational Approach Optimal Control Problems, by K.L. Teo, C.J. Goh, and K.H. Wong. This publication discusses the development of new theory and computational methods for solving various optimal control problems numerically and in a unified fashion. To keep the book accessible and uniform, it includes those results developed by the authors, their students, and their past and present collaborators. A brief review of methods that are not covered in this exposition, is also included. Knowledge gained from this book may inspire advancement of new techniques to solve complex problems that arise in the future. This book is intended as reference for researchers in mathematics, engineering, and other sciences, graduate students and practitioners who apply optimal control methods in their work. It may be appropriate reading material for a graduate level seminar or as a text for a course in optimal control.
