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This thesis will focus on the study of a theoretical and numerical approach for the multi-objective control problems with state constraints. Multi-objective optimization is an important approach for modelling complex problems in order to analyse the balance between different criteria to minimize. Here, the approach that will be used is based on the theory of Hamilton-Jacobi equations. The goal is to introduce a new methodology to study the properties and compute the Pareto front for multi-objective problems using the value function of an optimal control problem.
The theory of two-person, zero-sum differential games started at the be ginning of the 1960s with the works of R. Isaacs in the United States and L.S. Pontryagin and his school in the former Soviet Union. Isaacs based his work on the Dynamic Programming method. He analyzed many special cases of the partial differential equation now called Hamilton Jacobi-Isaacs-briefiy HJI-trying to solve them explicitly and synthe sizing optimal feedbacks from the solution. He began a study of singular surfaces that was continued mainly by J. Breakwell and P. Bernhard and led to the explicit solution of some low-dimensional but highly nontriv ial games; a recent survey of this theory can be found in the book by J. Lewin entitled Differential Games (Springer, 1994). Since the early stages of the theory, several authors worked on making the notion of value of a differential game precise and providing a rigorous derivation of the HJI equation, which does not have a classical solution in most cases; we mention here the works of W. Fleming, A. Friedman (see his book, Differential Games, Wiley, 1971), P.P. Varaiya, E. Roxin, R.J. Elliott and N.J. Kalton, N.N. Krasovskii, and A.I. Subbotin (see their book Po sitional Differential Games, Nauka, 1974, and Springer, 1988), and L.D. Berkovitz. A major breakthrough was the introduction in the 1980s of two new notions of generalized solution for Hamilton-Jacobi equations, namely, viscosity solutions, by M.G. Crandall and P.-L.
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 book focuses on various aspects of dynamic game theory, presenting state-of-the-art research and serving as a testament to the vitality and growth of the field of dynamic games and their applications. The selected contributions, written by experts in their respective disciplines, are outgrowths of presentations originally given at the 13th International Symposium of Dynamic Games and Applications held in Wrocław. The book covers a variety of topics, ranging from theoretical developments in game theory and algorithmic methods to applications, examples, and analysis in fields as varied as environmental management, finance and economics, engineering, guidance and control, and social interaction.
This work presents recent mathematical methods in the area of optimal control with a particular emphasis on the computational aspects and applications. Optimal control theory concerns the determination of control strategies for complex dynamical systems, in order to optimize some measure of their performance. Started in the 60's under the pressure of the "space race" between the US and the former USSR, the field now has a far wider scope, and embraces a variety of areas ranging from process control to traffic flow optimization, renewable resources exploitation and management of financial markets. These emerging applications require more and more efficient numerical methods for their solution, a very difficult task due the huge number of variables. The chapters of this volume give an up-to-date presentation of several recent methods in this area including fast dynamic programming algorithms, model predictive control and max-plus techniques. This book is addressed to researchers, graduate students and applied scientists working in the area of control problems, differential games and their applications.
This book is a self-contained account of the theory of viscosity solutions for first-order partial differential equations of Hamiltona "Jacobi type and its interplay with Bellmana (TM)s dynamic programming approach to optimal control and differential games, as it developed after the beginning of the 1980s with the pioneering work of M. Crandall and P.L. Lions. The book will be of interest to scientists involved in the theory of optimal control of deterministic linear and nonlinear systems. In particular, it will appeal to system theorists wishing to learn about a mathematical theory providing a correct framework for the classical method of dynamic programming as well as mathematicians interested in new methods for first-order nonlinear PDEs. The work may be used by graduate students and researchers in control theory both as an introductory textbook and as an up-to-date reference book. "The exposition is self-contained, clearly written and mathematically precise. The exercises and open problemsa ]will stimulate research in the field. The rich bibliography (over 530 titles) and the historical notes provide a useful guide to the area." a " Mathematical Reviews "With an excellent printing and clear structure (including an extensive subject and symbol registry) the book offers a deep insight into the praxis and theory of optimal control for the mathematically skilled reader. All sections close with suggestions for exercisesa ]Finally, with more than 500 cited references, an overview on the history and the main works of this modern mathematical discipline is given." a " ZAA "The minimal mathematical background...the detailed and clear proofs, the elegant style of presentation, and the sets of proposed exercises at the end of each section recommend this book, in the first place, as a lecture course for graduate students and as a manual for beginners in the field. However, this status is largely extended by the presence of many advanced topics and results by the fairly comprehensive and up-to-date bibliography and, particularly, by the very pertinent historical and bibliographical comments at the end of each chapter. In my opinion, this book is yet another remarkable outcome of the brilliant Italian School of Mathematics." a " Zentralblatt MATH "The book is based on some lecture notes taught by the authors at several universities...and selected parts of it can be used for graduate courses in optimal control. But it can be also used as a reference text for researchers (mathematicians and engineers)...In writing this book, the authors lend a great service to the mathematical community providing an accessible and rigorous treatment of a difficult subject." a " Acta Applicandae Mathematicae
These Lecture Notes contain the material relative to the courses given at the CIME summer school held in Cetraro, Italy from August 29 to September 3, 2011. The topic was "Hamilton-Jacobi Equations: Approximations, Numerical Analysis and Applications". The courses dealt mostly with the following subjects: first order and second order Hamilton-Jacobi-Bellman equations, properties of viscosity solutions, asymptotic behaviors, mean field games, approximation and numerical methods, idempotent analysis. The content of the courses ranged from an introduction to viscosity solutions to quite advanced topics, at the cutting edge of research in the field. We believe that they opened perspectives on new and delicate issues. These lecture notes contain four contributions by Yves Achdou (Finite Difference Methods for Mean Field Games), Guy Barles (An Introduction to the Theory of Viscosity Solutions for First-order Hamilton-Jacobi Equations and Applications), Hitoshi Ishii (A Short Introduction to Viscosity Solutions and the Large Time Behavior of Solutions of Hamilton-Jacobi Equations) and Grigory Litvinov (Idempotent/Tropical Analysis, the Hamilton-Jacobi and Bellman Equations).
This thesis addresses the construction of some algorithms for numerically solving optimal feedback control problems. Optimal control deals with the problem of finding a control law for a given system such that a certain optimality criterion is achieved. More precisely, optimal control problems involve a dynamic system with input quantities, called controls, and some quantity, called cost, to be minimized. An optimal control is a set of differential equations describing the paths of the control variables that optimise the cost. Finding solutions to problems of this nature involves a significantly high degree of difficulty in terms of cost and power compared with the related task of solving optimal open-loop control problems. Moreover, stability is a major problem in the feedback control problem, which may tend to overcorrect errors that can cause oscillations of constant or changing amplitude. A feedback control problem essentially depends on both state and time variables, and so its determination by numerical schemes has one serious drawback, it is the so called curse of dimensionality. Therefore, efficient numerical methods are needed for the accurate determination of optimal feedback controls. There are essentially two equivalent ways in widespread use today to solve optimal feedback control problems. In the first approach, often referred to as the direct approach, the optimal feedback control problem is approximated by considering the optimisation of an objective functional with respect to the control function. This optimisation is subject to the system dynamics and numerous constraints on the state and control variables. In the second approach, the optimal feedback control problem is transformed into a first order terminal value problem by formulating the problem as a nonlinear hyperbolic partial differential equation, known as the Hamilton-Jacobi-Bellman (HJB) equation. In this thesis we consider some numerical algorithms for solving the HJB equation, based on Radial Basis Functions (RBFs). We present a new adaptive least-squares collocation RBFs method for solving a HJB equation. The method involves the use of the least squares method using a set of RBFs in space variables, combined with the implicit backward Euler finite difference method in time, to create an unconditionally stable solution scheme. We also present some of the more theoretical aspects related to the solution of the HJB equation using the adaptive least-squares collocation RBFs method, especially, the relevant existence, uniqueness and stability results. We demonstrate the accuracy and effectiveness of this method by performing numerical experiments on test problems with up to three states and two control variables. Furthermore, we construct another numerical method based on a domain decomposition method using a matrix inversion technique for solving HJB equation. In this method, we propose a new formula for inverting nonsymmetric and full dense coefficient matrix faster than the classical matrix inversion techniques. We also investigate the accuracy of the numerical solution, condition numbers of the system matrix, and the computational time when increasing the number of subdomains. We perform some numerical experiments to illustrate the usefulness and accuracy of the method.