Adaptive Dynamic Programming For Control
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Adaptive Dynamic Programming Single And Multiple Controllers
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Author : Ruizhuo Song
language : en
Publisher: Springer
Release Date : 2018-12-28
Adaptive Dynamic Programming Single And Multiple Controllers written by Ruizhuo Song and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-28 with Technology & Engineering categories.
This book presents a class of novel optimal control methods and games schemes based on adaptive dynamic programming techniques. For systems with one control input, the ADP-based optimal control is designed for different objectives, while for systems with multi-players, the optimal control inputs are proposed based on games. In order to verify the effectiveness of the proposed methods, the book analyzes the properties of the adaptive dynamic programming methods, including convergence of the iterative value functions and the stability of the system under the iterative control laws. Further, to substantiate the mathematical analysis, it presents various application examples, which provide reference to real-world practices.
Adaptive Dynamic Programming For Control
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Author : Huaguang Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-14
Adaptive Dynamic Programming For Control written by Huaguang Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-14 with Technology & Engineering categories.
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-horizon control, implemented in discrete-time nonlinear systems showing the reader how to obtain suboptimal control solutions within a fixed number of control steps and with results more easily applied in real systems than those usually gained from infinite-horizon control; • nonlinear games for which a pair of mixed optimal policies are derived for solving games both when the saddle point does not exist, and, when it does, avoiding the existence conditions of the saddle point. Non-zero-sum games are studied in the context of a single network scheme in which policies are obtained guaranteeing system stability and minimizing the individual performance function yielding a Nash equilibrium. In order to make the coverage suitable for the student as well as for the expert reader, Adaptive Dynamic Programming in Discrete Time: • establishes the fundamental theory involved clearly with each chapter devoted to aclearly identifiable control paradigm; • demonstrates convergence proofs of the ADP algorithms to deepen understanding of the derivation of stability and convergence with the iterative computational methods used; and • shows how ADP methods can be put to use both in simulation and in real applications. This text will be of considerable interest to researchers interested in optimal control and its applications in operations research, applied mathematics computational intelligence and engineering. Graduate students working in control and operations research will also find the ideas presented here to be a source of powerful methods for furthering their study.
Adaptive Dynamic Programming With Applications In Optimal Control
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Author : Derong Liu
language : en
Publisher: Springer
Release Date : 2017-01-04
Adaptive Dynamic Programming With Applications In Optimal Control written by Derong Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-04 with Technology & Engineering categories.
This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors’ work: • renewable energy scheduling for smart power grids;• coal gasification processes; and• water–gas shift reactions. Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.
Approximate Dynamic Programming
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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2007-10-05
Approximate Dynamic Programming written by Warren B. Powell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-05 with Mathematics categories.
A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
Reinforcement Learning And Dynamic Programming Using Function Approximators
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Author : Lucian Busoniu
language : en
Publisher: CRC Press
Release Date : 2017-07-28
Reinforcement Learning And Dynamic Programming Using Function Approximators written by Lucian Busoniu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Computers categories.
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.
Predictive Control For Linear And Hybrid Systems
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Author : Francesco Borrelli
language : en
Publisher: Cambridge University Press
Release Date : 2017-06-22
Predictive Control For Linear And Hybrid Systems written by Francesco Borrelli and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-22 with Mathematics categories.
With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).
Optimal Adaptive Control And Differential Games By Reinforcement Learning Principles
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Author : Draguna L. Vrabie
language : en
Publisher: IET
Release Date : 2013
Optimal Adaptive Control And Differential Games By Reinforcement Learning Principles written by Draguna L. Vrabie and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.
The book reviews developments in the following fields: optimal adaptive control; online differential games; reinforcement learning principles; and dynamic feedback control systems.
Self Learning Optimal Control Of Nonlinear Systems
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Author : Qinglai Wei
language : en
Publisher:
Release Date : 2018
Self Learning Optimal Control Of Nonlinear Systems written by Qinglai Wei and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Flight Formation Control
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Author : Josep M. Guerrero
language : en
Publisher: John Wiley & Sons
Release Date : 2012-12-17
Flight Formation Control written by Josep M. Guerrero and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-17 with Technology & Engineering categories.
In the last decade the development and control of Unmanned Aerial Vehicles (UAVs) has attracted a lot of interest. Both researchers and companies have a growing interest in improving this type of vehicle given their many civilian and military applications. This book presents the state of the art in the area of UAV Flight Formation. The coordination and robust consensus approaches are presented in detail as well as formation flight control strategies which are validated in experimental platforms. It aims at helping students and academics alike to better understand what coordination and flight formation control can make possible. Several novel methods are presented: - controllability and observability of multi-agent systems; - robust consensus; - flight formation control; - stability of formations over noisy networks; which generate solutions of guaranteed performance for UAV Flight Formation. Contents 1. Introduction, J.A. Guerrero. 2. Theoretical Preliminaries, J.A. Guerrero. 3. Multiagent Coordination Strategies, J.A. Guerrero, R. Lozano, M.W. Spong, N. Chopra. 4. Robust Control Design for Multiagent Systems with Parametric Uncertainty, J.A. Guerrero, G. Romero. 5. On Adaptive and Robust Controlled Synchronization of Networked Robotic Systems on Strongly Connected Graphs, Y.-C. Liu, N. Chopra. 6. Modeling and Control of Mini UAV, G. Flores Colunga, J.A. Guerrero, J. Escareño, R. Lozano. 7. Flight Formation Control Strategies for Mini UAVs, J.A. Guerrero. 8. Formation Based on Potential Functions, L. García, A. Dzul. 9. Quadrotor Vision-Based Control, J.E. Gomez-Balderas, J.A. Guerrero, S. SALAZAR, R. Lozano, P. Castillo. 10. Toward Vision-Based Coordination of Quadrotor Platoons, L.R. García Carrillo, J.A. Guerrero, R. Lozano. 11. Optimal Guidance for Rotorcraft Platoon Formation Flying in Wind Fields, J.A. Guerrero, Y. Bestaoui, R. Lozano. 12. Impact of Wireless Medium Access Protocol on the Quadrotor Formation Control, J.A. Guerrero, Y. Challal, P. Castillo. 13. MAC Protocol for Wireless Communications, A. Mendez, M. Panduro, O. Elizarraras, D. Covarrubias. 14. Optimization of a Scannable Pattern for Bidimensional Antenna Arrays to Provide Maximum Performance, A. Reyna, M.A. Panduro, A. Mendez.
Applied Dynamic Programming
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Author : Richard E. Bellman
language : en
Publisher: Princeton University Press
Release Date : 2015-12-08
Applied Dynamic Programming written by Richard E. Bellman and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-08 with Computers categories.
This comprehensive study of dynamic programming applied to numerical solution of optimization problems. It will interest aerodynamic, control, and industrial engineers, numerical analysts, and computer specialists, applied mathematicians, economists, and operations and systems analysts. Originally published in 1962. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.