Reinforcement Learning for Optimal Feedback Control A Lyapunov-Based
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
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Rushikesh Kamalapurkar Patrick Walters · Joel Rosenfeld Warren Dixon
Reinforcement Learning for Optimal Feedback Control A Lyapunov-Based Approach
Communications and Control Engineering Series editors Alberto Isidori, Roma, Italy Jan H. van Schuppen, Amsterdam, The Netherlands Eduardo D. Sontag, Boston, USA Miroslav Krstic, La Jolla, USA
Communications and Control Engineering is a high-level academic monograph series publishing research in control and systems theory, control engineering and communications. It has worldwide distribution to engineers, researchers, educators (several of the titles in this series find use as advanced textbooks although that is not their primary purpose), and libraries. The series reflects the major technological and mathematical advances that have a great impact in the fields of communication and control. The range of areas to which control and systems theory is applied is broadening rapidly with particular growth being noticeable in the fields of finance and biologically-inspired control. Books in this series generally pull together many related research threads in more mature areas of the subject than the highly-specialised volumes of Lecture Notes in Control and Information Sciences. This series’s mathematical and control-theoretic emphasis is complemented by Advances in Industrial Control which provides a much more applied, engineering-oriented outlook. Publishing Ethics: Researchers should conduct their research from research proposal to publication in line with best practices and codes of conduct of relevant professional bodies and/or national and international regulatory bodies. For more details on individual ethics matters please see: https://www.springer.com/gp/authors-editors/journal-author/journal-author-helpdesk/publishing-ethics/14214.
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Rushikesh Kamalapurkar Patrick Walters Joel Rosenfeld Warren Dixon •
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Reinforcement Learning for Optimal Feedback Control A Lyapunov-Based Approach
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Rushikesh Kamalapurkar Mechanical and Aerospace Engineering Oklahoma State University Stillwater, OK USA
Joel Rosenfeld Electrical Engineering Vanderbilt University Nashville, TN USA
Patrick Walters Naval Surface Warfare Center Panama City, FL USA
Warren Dixon Department of Mechanical and Aerospace Engineering University of Florida Gainesville, FL USA
ISSN 0178-5354 ISSN 2197-7119 (electronic) Communications and Control Engineering ISBN 978-3-319-78383-3 ISBN 978-3-319-78384-0 (eBook) https://doi.org/10.1007/978-3-319-78384-0 Library of Congress Control Number: 2018936639 MATLAB® and Simulink® are registered trademarks of The MathWorks, Inc., 1 Apple Hill Drive, Natick, MA 01760-2098, USA, http://www.mathworks.com. Mathematics Subject Classification (2010): 49-XX, 34-XX, 46-XX, 65-XX, 68-XX, 90-XX, 91-XX, 93-XX © Springer International Publishing AG 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights
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