Dynamic Programming And Optimal Control Approximate Dynamic Programming

Dynamic Programming and Optimal Control  Approximate dynamic programming PDF Book Details:
Author: Dimitri P. Bertsekas
Publisher:
ISBN: 9781886529441
Size: 65.13 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 694
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Dynamic Programming And Optimal Control Approximate Dynamic Programming PDF

by Dimitri P. Bertsekas, Dynamic Programming And Optimal Control Approximate Dynamic Programming Books available in PDF, EPUB, Mobi Format. Download Dynamic Programming And Optimal Control Approximate Dynamic Programming books,


Handbook Of Learning And Approximate Dynamic Programming

Handbook of Learning and Approximate Dynamic Programming PDF Book Details:
Author: Jennie Si
Publisher: John Wiley & Sons
ISBN: 9780471660545
Size: 43.85 MB
Format: PDF, Mobi
Category : Technology & Engineering
Languages : en
Pages : 672
View: 4794

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Handbook Of Learning And Approximate Dynamic Programming PDF

by Jennie Si, Handbook Of Learning And Approximate Dynamic Programming Books available in PDF, EPUB, Mobi Format. Download Handbook Of Learning And Approximate Dynamic Programming books, A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented The contributors are leading researchers in the field


Approximate Dynamic Programming Based Solutions For Fixed Final Time Optimal Control And Optimal Switching

Approximate Dynamic Programming Based Solutions for Fixed final time Optimal Control and Optimal Switching PDF Book Details:
Author: Ali Heydari
Publisher:
ISBN:
Size: 27.77 MB
Format: PDF
Category : Automatic programming (Computer science)
Languages : en
Pages : 239
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Approximate Dynamic Programming Based Solutions For Fixed Final Time Optimal Control And Optimal Switching PDF

by Ali Heydari, Approximate Dynamic Programming Based Solutions For Fixed Final Time Optimal Control And Optimal Switching Books available in PDF, EPUB, Mobi Format. Download Approximate Dynamic Programming Based Solutions For Fixed Final Time Optimal Control And Optimal Switching books, "Optimal solutions with neural networks (NN) based on an approximate dynamic programming (ADP) framework for new classes of engineering and non-engineering problems and associated difficulties and challenges are investigated in this dissertation. In the enclosed eight papers, the ADP framework is utilized for solving fixed-final-time problems (also called terminal control problems) and problems with switching nature. An ADP based algorithm is proposed in Paper 1 for solving fixed-final-time problems with soft terminal constraint, in which, a single neural network with a single set of weights is utilized. Paper 2 investigates fixed-final-time problems with hard terminal constraints. The optimality analysis of the ADP based algorithm for fixed-final-time problems is the subject of Paper 3, in which, it is shown that the proposed algorithm leads to the global optimal solution providing certain conditions hold. Afterwards, the developments in Papers 1 to 3 are used to tackle a more challenging class of problems, namely, optimal control of switching systems. This class of problems is divided into problems with fixed mode sequence (Papers 4 and 5) and problems with free mode sequence (Papers 6 and 7). Each of these two classes is further divided into problems with autonomous subsystems (Papers 4 and 6) and problems with controlled subsystems (Papers 5 and 7). Different ADP-based algorithms are developed and proofs of convergence of the proposed iterative algorithms are presented. Moreover, an extension to the developments is provided for online learning of the optimal switching solution for problems with modeling uncertainty in Paper 8. Each of the theoretical developments is numerically analyzed using different real-world or benchmark problems"--Abstract, page v.


Approximate Dynamic Programming

Approximate Dynamic Programming PDF Book Details:
Author: Dimitri P. Bertsekas
Publisher:
ISBN: 9781886529083
Size: 64.23 MB
Format: PDF, Kindle
Category :
Languages : en
Pages :
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Approximate Dynamic Programming PDF

by Dimitri P. Bertsekas, Approximate Dynamic Programming Books available in PDF, EPUB, Mobi Format. Download Approximate Dynamic Programming books,


Self Learning Optimal Control Of Nonlinear Systems

Self Learning Optimal Control of Nonlinear Systems PDF Book Details:
Author: Qinglai Wei
Publisher: Springer
ISBN: 981104080X
Size: 46.91 MB
Format: PDF, Docs
Category : Technology & Engineering
Languages : en
Pages : 230
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Self Learning Optimal Control Of Nonlinear Systems PDF

by Qinglai Wei, Self Learning Optimal Control Of Nonlinear Systems Books available in PDF, EPUB, Mobi Format. Download Self Learning Optimal Control Of Nonlinear Systems books, This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.


A Study On Architecture Algorithms And Applications Of Approximate Dynamic Programming Based Approach To Optimal Control

A Study on Architecture  Algorithms  and Applications of Approximate Dynamic Programming Based Approach to Optimal Control PDF Book Details:
Author: Jong Min Lee
Publisher:
ISBN:
Size: 36.30 MB
Format: PDF, Mobi
Category : Dynamic programimng
Languages : en
Pages :
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A Study On Architecture Algorithms And Applications Of Approximate Dynamic Programming Based Approach To Optimal Control PDF

by Jong Min Lee, A Study On Architecture Algorithms And Applications Of Approximate Dynamic Programming Based Approach To Optimal Control Books available in PDF, EPUB, Mobi Format. Download A Study On Architecture Algorithms And Applications Of Approximate Dynamic Programming Based Approach To Optimal Control books, This thesis develops approximate dynamic programming (ADP) strategies suitable for process control problems aimed at overcoming the limitations of MPC, which are the potentially exorbitant on-line computational requirement and the inability to consider the future interplay between uncertainty and estimation in the optimal control calculation. The suggested approach solves the DP only for the state points visited by closed-loop simulations with judiciously chosen control policies. The approach helps us combat a well-known problem of the traditional DP called 'curse-of-dimensionality, ' while it allows the user to derive an improved control policy from the initial ones. The critical issue of the suggested method is a proper choice and design of function approximator. A local averager with a penalty term is proposed to guarantee a stably learned control policy as well as acceptable on-line performance. The thesis also demonstrates versatility of the proposed ADP strategy with difficult process control problems. First, a stochastic adaptive control problem is presented. In this application an ADP-based control policy shows an "active" probing property to reduce uncertainties, leading to a better control performance. The second example is a dual-mode controller, which is a supervisory scheme that actively prevents the progression of abnormal situations under a local controller at their onset. Finally, two ADP strategies for controlling nonlinear processes based on input-output data are suggested. They are model-based and model-free approaches, and have the advantage of conveniently incorporating the knowledge of identification data distribution into the control calculation with performance improvement.


Der C Programmierer

Der C   Programmierer PDF Book Details:
Author: Ulrich Breymann
Publisher:
ISBN: 9783446443464
Size: 33.86 MB
Format: PDF, ePub, Mobi
Category :
Languages : de
Pages : 992
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Der C Programmierer PDF

by Ulrich Breymann, Der C Programmierer Books available in PDF, EPUB, Mobi Format. Download Der C Programmierer books,


C

C   PDF Book Details:
Author: Torsten T. Will
Publisher:
ISBN: 9783836275934
Size: 10.80 MB
Format: PDF, Kindle
Category : Computers
Languages : de
Pages : 1150
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C PDF

by Torsten T. Will, C Books available in PDF, EPUB, Mobi Format. Download C books,


Robust Adaptive Dynamic Programming

Robust Adaptive Dynamic Programming PDF Book Details:
Author: Yu Jiang
Publisher: John Wiley & Sons
ISBN: 1119132665
Size: 63.92 MB
Format: PDF, Kindle
Category : Science
Languages : en
Pages : 216
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Robust Adaptive Dynamic Programming PDF

by Yu Jiang, Robust Adaptive Dynamic Programming Books available in PDF, EPUB, Mobi Format. Download Robust Adaptive Dynamic Programming books, A comprehensive look at state-of-the-art ADP theory and real-world applications This book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven optimal control design challenges arising from both parametric and dynamic uncertainties. Traditional model-based approaches leave much to be desired when addressing the challenges posed by the ever-increasing complexity of real-world engineering systems. An alternative which has received much interest in recent years are biologically-inspired approaches, primarily RADP. Despite their growing popularity worldwide, until now books on ADP have focused nearly exclusively on analysis and design, with scant consideration given to how it can be applied to address robustness issues, a new challenge arising from dynamic uncertainties encountered in common engineering problems. Robust Adaptive Dynamic Programming zeros in on the practical concerns of engineers. The authors develop RADP theory from linear systems to partially-linear, large-scale, and completely nonlinear systems. They provide in-depth coverage of state-of-the-art applications in power systems, supplemented with numerous real-world examples implemented in MATLAB. They also explore fascinating reverse engineering topics, such how ADP theory can be applied to the study of the human brain and cognition. In addition, the book: Covers the latest developments in RADP theory and applications for solving a range of systems’ complexity problems Explores multiple real-world implementations in power systems with illustrative examples backed up by reusable MATLAB code and Simulink block sets Provides an overview of nonlinear control, machine learning, and dynamic control Features discussions of novel applications for RADP theory, including an entire chapter on how it can be used as a computational mechanism of human movement control Robust Adaptive Dynamic Programming is both a valuable working resource and an intriguing exploration of contemporary ADP theory and applications for practicing engineers and advanced students in systems theory, control engineering, computer science, and applied mathematics.


Reinforcement Learning And Optimal Control

Reinforcement Learning and Optimal Control PDF Book Details:
Author: Dimitri Bertsekas
Publisher: Athena Scientific
ISBN: 1886529396
Size: 61.12 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 388
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Reinforcement Learning And Optimal Control PDF

by Dimitri Bertsekas, Reinforcement Learning And Optimal Control Books available in PDF, EPUB, Mobi Format. Download Reinforcement Learning And Optimal Control books, This book considers large and challenging multistage decision problems, which can be solved in principle by dynamic programming (DP), but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively known by several essentially equivalent names: reinforcement learning, approximate dynamic programming, neuro-dynamic programming. They have been at the forefront of research for the last 25 years, and they underlie, among others, the recent impressive successes of self-learning in the context of games such as chess and Go. Our subject has benefited greatly from the interplay of ideas from optimal control and from artificial intelligence, as it relates to reinforcement learning and simulation-based neural network methods. One of the aims of the book is to explore the common boundary between these two fields and to form a bridge that is accessible by workers with background in either field. Another aim is to organize coherently the broad mosaic of methods that have proved successful in practice while having a solid theoretical and/or logical foundation. This may help researchers and practitioners to find their way through the maze of competing ideas that constitute the current state of the art. This book relates to several of our other books: Neuro-Dynamic Programming (Athena Scientific, 1996), Dynamic Programming and Optimal Control (4th edition, Athena Scientific, 2017), Abstract Dynamic Programming (2nd edition, Athena Scientific, 2018), and Nonlinear Programming (Athena Scientific, 2016). However, the mathematical style of this book is somewhat different. While we provide a rigorous, albeit short, mathematical account of the theory of finite and infinite horizon dynamic programming, and some fundamental approximation methods, we rely more on intuitive explanations and less on proof-based insights. Moreover, our mathematical requirements are quite modest: calculus, a minimal use of matrix-vector algebra, and elementary probability (mathematically complicated arguments involving laws of large numbers and stochastic convergence are bypassed in favor of intuitive explanations). The book illustrates the methodology with many examples and illustrations, and uses a gradual expository approach, which proceeds along four directions: (a) From exact DP to approximate DP: We first discuss exact DP algorithms, explain why they may be difficult to implement, and then use them as the basis for approximations. (b) From finite horizon to infinite horizon problems: We first discuss finite horizon exact and approximate DP methodologies, which are intuitive and mathematically simple, and then progress to infinite horizon problems. (c) From deterministic to stochastic models: We often discuss separately deterministic and stochastic problems, since deterministic problems are simpler and offer special advantages for some of our methods. (d) From model-based to model-free implementations: We first discuss model-based implementations, and then we identify schemes that can be appropriately modified to work with a simulator. The book is related and supplemented by the companion research monograph Rollout, Policy Iteration, and Distributed Reinforcement Learning (Athena Scientific, 2020), which focuses more closely on several topics related to rollout, approximate policy iteration, multiagent problems, discrete and Bayesian optimization, and distributed computation, which are either discussed in less detail or not covered at all in the present book. The author's website contains class notes, and a series of videolectures and slides from a 2021 course at ASU, which address a selection of topics from both books.