Adaptive Dynamic Programming: Single and Multiple Controllers

Adaptive Dynamic Programming: Single and Multiple Controllers
Title Adaptive Dynamic Programming: Single and Multiple Controllers PDF eBook
Author Ruizhuo Song
Publisher Springer
Pages 278
Release 2018-12-28
Genre Technology & Engineering
ISBN 9811317127

Download Adaptive Dynamic Programming: Single and Multiple Controllers Book in PDF, Epub and Kindle

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

Adaptive Dynamic Programming
Title Adaptive Dynamic Programming PDF eBook
Author Jiayue Sun
Publisher Springer Nature
Pages 144
Release 2023-10-14
Genre Technology & Engineering
ISBN 9819959292

Download Adaptive Dynamic Programming Book in PDF, Epub and Kindle

This open access book focuses on the practical application of Adaptive Dynamic Programming (ADP) in chemotherapy drug delivery, taking into account clinical variables and real-time data. ADP's ability to adapt to changing conditions and make optimal decisions in complex and uncertain situations makes it a valuable tool in addressing pressing challenges in healthcare and other fields. As optimization technology evolves, we can expect to see even more sophisticated and powerful solutions emerge.

Robust Adaptive Dynamic Programming

Robust Adaptive Dynamic Programming
Title Robust Adaptive Dynamic Programming PDF eBook
Author Yu Jiang
Publisher John Wiley & Sons
Pages 220
Release 2017-04-13
Genre Science
ISBN 1119132657

Download Robust Adaptive Dynamic Programming Book in PDF, Epub and Kindle

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 Approximate Dynamic Programming for Feedback Control

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
Title Reinforcement Learning and Approximate Dynamic Programming for Feedback Control PDF eBook
Author Frank L. Lewis
Publisher John Wiley & Sons
Pages 498
Release 2013-01-28
Genre Technology & Engineering
ISBN 1118453972

Download Reinforcement Learning and Approximate Dynamic Programming for Feedback Control Book in PDF, Epub and Kindle

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.

Learning-Based Control

Learning-Based Control
Title Learning-Based Control PDF eBook
Author Zhong-Ping Jiang
Publisher Now Publishers
Pages 122
Release 2020-12-07
Genre Technology & Engineering
ISBN 9781680837520

Download Learning-Based Control Book in PDF, Epub and Kindle

The recent success of Reinforcement Learning and related methods can be attributed to several key factors. First, it is driven by reward signals obtained through the interaction with the environment. Second, it is closely related to the human learning behavior. Third, it has a solid mathematical foundation. Nonetheless, conventional Reinforcement Learning theory exhibits some shortcomings particularly in a continuous environment or in considering the stability and robustness of the controlled process. In this monograph, the authors build on Reinforcement Learning to present a learning-based approach for controlling dynamical systems from real-time data and review some major developments in this relatively young field. In doing so the authors develop a framework for learning-based control theory that shows how to learn directly suboptimal controllers from input-output data. There are three main challenges on the development of learning-based control. First, there is a need to generalize existing recursive methods. Second, as a fundamental difference between learning-based control and Reinforcement Learning, stability and robustness are important issues that must be addressed for the safety-critical engineering systems such as self-driving cars. Third, data efficiency of Reinforcement Learning algorithms need be addressed for safety-critical engineering systems. This monograph provides the reader with an accessible primer on a new direction in control theory still in its infancy, namely Learning-Based Control Theory, that is closely tied to the literature of safe Reinforcement Learning and Adaptive Dynamic Programming.

Advances in Neural Computation, Machine Learning, and Cognitive Research VII

Advances in Neural Computation, Machine Learning, and Cognitive Research VII
Title Advances in Neural Computation, Machine Learning, and Cognitive Research VII PDF eBook
Author Boris Kryzhanovsky
Publisher Springer Nature
Pages 505
Release 2023-11-12
Genre Technology & Engineering
ISBN 3031448650

Download Advances in Neural Computation, Machine Learning, and Cognitive Research VII Book in PDF, Epub and Kindle

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXV International Conference on Neuroinformatics, held on October 23-27, 2023, in Moscow, Russia.

Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles
Title Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles PDF eBook
Author Draguna L. Vrabie
Publisher IET
Pages 305
Release 2013
Genre Computers
ISBN 1849194890

Download Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles Book in PDF, Epub and Kindle

The book reviews developments in the following fields: optimal adaptive control; online differential games; reinforcement learning principles; and dynamic feedback control systems.