Download or read book Infinite Horizon Optimal Control in the Discrete Time Framework written by Joël Blot and published by Springer Science & Business Media. This book was released on 2013-11-08 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​​​​In this book the authors take a rigorous look at the infinite-horizon discrete-time optimal control theory from the viewpoint of Pontryagin’s principles. Several Pontryagin principles are described which govern systems and various criteria which define the notions of optimality, along with a detailed analysis of how each Pontryagin principle relate to each other. The Pontryagin principle is examined in a stochastic setting and results are given which generalize Pontryagin’s principles to multi-criteria problems. ​Infinite-Horizon Optimal Control in the Discrete-Time Framework is aimed toward researchers and PhD students in various scientific fields such as mathematics, applied mathematics, economics, management, sustainable development (such as, of fisheries and of forests), and Bio-medical sciences who are drawn to infinite-horizon discrete-time optimal control problems.

Download or read book Infinite Horizon Optimal Control written by Dean A. Carlson and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph deals with various classes of deterministic continuous time optimal control problems wh ich are defined over unbounded time intervala. For these problems, the performance criterion is described by an improper integral and it is possible that, when evaluated at a given admissible element, this criterion is unbounded. To cope with this divergence new optimality concepts; referred to here as "overtaking", "weakly overtaking", "agreeable plans", etc. ; have been proposed. The motivation for studying these problems arisee primarily from the economic and biological aciences where models of this nature arise quite naturally since no natural bound can be placed on the time horizon when one considers the evolution of the state of a given economy or species. The reeponsibility for the introduction of this interesting class of problems rests with the economiste who first studied them in the modeling of capital accumulation processes. Perhaps the earliest of these was F. Ramsey who, in his seminal work on a theory of saving in 1928, considered a dynamic optimization model defined on an infinite time horizon. Briefly, this problem can be described as a "Lagrange problem with unbounded time interval". The advent of modern control theory, particularly the formulation of the famoue Maximum Principle of Pontryagin, has had a considerable impact on the treatment of these models as well as optimization theory in general.

Download or read book Contr le Optimal en Temps Discret Et en Horizon Infini written by Thoi-Nhan Ngo and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis contains original contributions to the optimal control theory in the discrete-time framework and in infinite horizon following the viewpoint of Pontryagin. There are 5 chapters in this thesis. In Chapter 1, we recall preliminary results on sequence spaces and on differential calculus in normed linear space. In Chapter 2, we study a single-objective optimal control problem in discrete-time framework and in infinite horizon with an asymptotic constraint and with autonomous system. We use an approach of functional analytic for this problem after translating it into the form of an optimization problem in Banach (sequence) spaces. Then a weak Pontyagin principle is established for this problem by using a classical multiplier rule in Banach spaces. In Chapter 3, we establish a strong Pontryagin principle for the problems considered in Chapter 2 using a result of Ioffe and Tihomirov. Chapter 4 is devoted to the problems of Optimal Control, in discrete time framework and in infinite horizon, which are more general with several different criteria. The used method is the reduction to finite-horizon initiated by J. Blot and H. Chebbi in 2000. The considered problems are governed by difference equations or difference inequations. A new weak Pontryagin principle is established using a recent result of J. Blot on the Fritz John multipliers. Chapter 5 deals with the multicriteria optimal control problems in discrete time framework and infinite horizon. New weak and strong Pontryagin principles are established, again using recent optimization results, under lighter assumptions than existing ones.

Download or read book Stochastic Optimal Control written by Dimitri P. Bertsekas and published by . This book was released on 1961 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Approximating Infinite Horizon Discrete time Optimal Control Using CMAC Networks written by Eric J. Barth and published by . This book was released on 2000 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Frontiers Of Intelligent Control And Information Processing written by Derong Liu and published by World Scientific. This book was released on 2014-08-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: The current research and development in intelligent control and information processing have been driven increasingly by advancements made from fields outside the traditional control areas, into new frontiers of intelligent control and information processing so as to deal with ever more complex systems with ever growing size of data and complexity.As researches in intelligent control and information processing are taking on ever more complex problems, the control system as a nuclear to coordinate the activity within a system increasingly need to be equipped with the capability to analyze, and reason so as to make decision. This requires the support of cognitive components, and communication protocol to synchronize events within the system to operate in unison.In this review volume, we invited several well-known experts and active researchers from adaptive/approximate dynamic programming, reinforcement learning, machine learning, neural optimal control, networked systems, and cyber-physical systems, online concept drift detection, pattern recognition, to contribute their most recent achievements into the development of intelligent control systems, to share with the readers, how these inclusions helps to enhance the cognitive capability of future control systems in handling complex problems.This review volume encapsulates the state-of-art pioneering works in the development of intelligent control systems. Proposition and evocations of each solution is backed up with evidences from applications, could be used as references for the consideration of decision support and communication components required for today intelligent control systems.

Download or read book Essays on Pareto Optimality in Cooperative Games written by Yaning Lin and published by Springer Nature. This book was released on 2022-09-21 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on Pareto optimality in cooperative games. Most of the existing works focus on the Pareto optimality of deterministic continuous-time systems or for the regular convex LQ case. To expand on the available literature, we explore the existence conditions of Pareto solutions in stochastic differential game for more general cases. In addition, the LQ Pareto game for stochastic singular systems, Pareto-based guaranteed cost control for uncertain mean-field stochastic systems, and the existence conditions of Pareto solutions in cooperative difference game are also studied in detail. Addressing Pareto optimality for more general cases and wider systems is one of the major features of the book, making it particularly suitable for readers who are interested in multi-objective optimal control. Accordingly, it offers a valuable asset for researchers, engineers, and graduate students in the fields of control theory and control engineering, economics, management science, mathematics, etc.

Download or read book Optimization and Approximation written by Pablo Pedregal and published by Springer. This book was released on 2017-09-07 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a basic, initial resource, introducing science and engineering students to the field of optimization. It covers three main areas: mathematical programming, calculus of variations and optimal control, highlighting the ideas and concepts and offering insights into the importance of optimality conditions in each area. It also systematically presents affordable approximation methods. Exercises at various levels have been included to support the learning process.

Download or read book Discrete Time Optimal Control and Games on Large Intervals written by Alexander J. Zaslavski and published by Springer. This book was released on 2017-04-03 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Devoted to the structure of approximate solutions of discrete-time optimal control problems and approximate solutions of dynamic discrete-time two-player zero-sum games, this book presents results on properties of approximate solutions in an interval that is independent lengthwise, for all sufficiently large intervals. Results concerning the so-called turnpike property of optimal control problems and zero-sum games in the regions close to the endpoints of the time intervals are the main focus of this book. The description of the structure of approximate solutions on sufficiently large intervals and its stability will interest graduate students and mathematicians in optimal control and game theory, engineering, and economics. This book begins with a brief overview and moves on to analyze the structure of approximate solutions of autonomous nonconcave discrete-time optimal control Lagrange problems.Next the structures of approximate solutions of autonomous discrete-time optimal control problems that are discrete-time analogs of Bolza problems in calculus of variations are studied. The structures of approximate solutions of two-player zero-sum games are analyzed through standard convexity-concavity assumptions. Finally, turnpike properties for approximate solutions in a class of nonautonomic dynamic discrete-time games with convexity-concavity assumptions are examined.

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.​

Download or read book Stochastic Optimal Control The Discrete Time Case written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 1996-12-01 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph, first published in 1978 by Academic Press, remains the authoritative and comprehensive treatment of the mathematical foundations of stochastic optimal control of discrete-time systems, including the treatment of the intricate measure-theoretic issues. It is an excellent supplement to the first author's Dynamic Programming and Optimal Control (Athena Scientific, 2018). Review of the 1978 printing:"Bertsekas and Shreve have written a fine book. The exposition is extremely clear and a helpful introductory chapter provides orientation and a guide to the rather intimidating mass of literature on the subject. Apart from anything else, the book serves as an excellent introduction to the arcane world of analytic sets and other lesser known byways of measure theory." Mark H. A. Davis, Imperial College, in IEEE Trans. on Automatic Control Among its special features, the book: 1) Resolves definitively the mathematical issues of discrete-time stochastic optimal control problems, including Borel models, and semi-continuous models 2) Establishes the most general possible theory of finite and infinite horizon stochastic dynamic programming models, through the use of analytic sets and universally measurable policies 3) Develops general frameworks for dynamic programming based on abstract contraction and monotone mappings 4) Provides extensive background on analytic sets, Borel spaces and their probability measures 5) Contains much in depth research not found in any other textbook

Download or read book Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory written by Timothy L. Molloy and published by Springer Nature. This book was released on 2022-02-18 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a novel unified treatment of inverse problems in optimal control and noncooperative dynamic game theory. It provides readers with fundamental tools for the development of practical algorithms to solve inverse problems in control, robotics, biology, and economics. The treatment involves the application of Pontryagin's minimum principle to a variety of inverse problems and proposes algorithms founded on the elegance of dynamic optimization theory. There is a balanced emphasis between fundamental theoretical questions and practical matters. The text begins by providing an introduction and background to its topics. It then discusses discrete-time and continuous-time inverse optimal control. The focus moves on to differential and dynamic games and the book is completed by consideration of relevant applications. The algorithms and theoretical results developed in Inverse Optimal Control and Inverse Noncooperative Dynamic Game Theory provide new insights into information requirements for solving inverse problems, including the structure, quantity, and types of state and control data. These insights have significant practical consequences in the design of technologies seeking to exploit inverse techniques such as collaborative robots, driver-assistance technologies, and autonomous systems. The book will therefore be of interest to researchers, engineers, and postgraduate students in several disciplines within the area of control and robotics.

Download or read book Constrained Control and Estimation written by Graham Goodwin and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent developments in constrained control and estimation have created a need for this comprehensive introduction to the underlying fundamental principles. These advances have significantly broadened the realm of application of constrained control. - Using the principal tools of prediction and optimisation, examples of how to deal with constraints are given, placing emphasis on model predictive control. - New results combine a number of methods in a unique way, enabling you to build on your background in estimation theory, linear control, stability theory and state-space methods. - Companion web site, continually updated by the authors. Easy to read and at the same time containing a high level of technical detail, this self-contained, new approach to methods for constrained control in design will give you a full understanding of the subject.

Download or read book Advanced Optimal Control and Applications Involving Critic Intelligence written by Ding Wang and published by Springer Nature. This book was released on 2023-01-21 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book intends to report new optimal control results with critic intelligence for complex discrete-time systems, which covers the novel control theory, advanced control methods, and typical applications for wastewater treatment systems. Therein, combining with artificial intelligence techniques, such as neural networks and reinforcement learning, the novel intelligent critic control theory as well as a series of advanced optimal regulation and trajectory tracking strategies are established for discrete-time nonlinear systems, followed by application verifications to complex wastewater treatment processes. Consequently, developing such kind of critic intelligence approaches is of great significance for nonlinear optimization and wastewater recycling. The book is likely to be of interest to researchers and practitioners as well as graduate students in automation, computer science, and process industry who wish to learn core principles, methods, algorithms, and applications in the field of intelligent optimal control. It is beneficial to promote the development of intelligent optimal control approaches and the construction of high-level intelligent systems.

Download or read book Reinforcement Learning for Optimal Feedback Control written by Rushikesh Kamalapurkar and published by Springer. This book was released on 2018-05-10 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book’s focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor–critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements. This monograph provides academic researchers with backgrounds in diverse disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.

Download or read book Reinforcement Learning for Sequential Decision and Optimal Control written by Shengbo Eben Li and published by Springer Nature. This book was released on 2023-04-05 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman’s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on.

Download or read book Turnpike Theory of Continuous Time Linear Optimal Control Problems written by Alexander J. Zaslavski and published by Springer. This book was released on 2015-07-01 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Individual turnpike results are of great interest due to their numerous applications in engineering and in economic theory; in this book the study is focused on new results of turnpike phenomenon in linear optimal control problems. The book is intended for engineers as well as for mathematicians interested in the calculus of variations, optimal control and in applied functional analysis. Two large classes of problems are studied in more depth. The first class studied in Chapter 2 consists of linear control problems with periodic nonsmooth convex integrands. Chapters 3-5 consist of linear control problems with autonomous convex smooth integrands. Chapter 6 discusses a turnpike property for dynamic zero-sum games with linear constraints. Chapter 7 examines genericity results. In Chapter 8, the description of structure of variational problems with extended-valued integrands is obtained. Chapter 9 ends the exposition with a study of turnpike phenomenon for dynamic games with extended value integrands.