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Changsheng Hua - Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

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Changsheng Hua Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems: summary, description and annotation

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Changsheng Hua proposes two approaches, an input/output recovery approach and a performance index-based approach for robustness and performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author develops Q-learning and natural actor-critic (NAC) methods, respectively. Their effectiveness has been demonstrated by an experimental study on a brushless direct current motor test rig.


The author:

Changsheng Hua received the Ph.D. degree at the Institute of Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany, in 2020. His research interests include model-based and data-driven fault diagnosis and fault-tolerant techniques.

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Book cover of Reinforcement Learning Aided Performance Optimization of Feedback - photo 1
Book cover of Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
Changsheng Hua
Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
1st ed. 2021
Logo of the publisher Changsheng Hua Duisburg Germany ISBN - photo 2
Logo of the publisher
Changsheng Hua
Duisburg, Germany
ISBN 978-3-658-33033-0 e-ISBN 978-3-658-33034-7
https://doi.org/10.1007/978-3-658-33034-7

Von der Fakultt fr Ingenieurwissenschaften, Abteilung Elektrotechnik und Informationstechnik der Universitt Duisburg-Essen zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften (Dr.-Ing.) genehmigte Dissertation von Changsheng Hua aus Jiangsu, V.R. China.

1. Gutachter: Prof. Dr.-Ing. Steven X. Ding

2. Gutachter: Prof. Dr. Yuri A.W. Shardt

Tag der mndlichen Prfung: 23.01.2020

The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021
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 of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible Editor: Stefanie Eggert

This Springer Vieweg imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature.

The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

To my parents, my brother, and my wife Xiaodi

Acknowledgments

First and foremost, I am deeply indebted to my advisor, Prof. Dr.-Ing. Steven X. Ding, for his guidance, encouragement and insightful discussions during my Ph.D. studies. He has continually pointed me in the right direction and provided me inspiration to do the best work I could. I would also like to express my heartfelt appreciation to Prof. Dr. Yuri A.W. Shardt, who has sparked my interest in performance optimization of control systems and mentored me a lot. He has shared with me his rich experience in academic research and scientific writing. I feel very lucky to have him as one of my major collaborators.

I would particularly like to give thanks to Dr.-Ing. Linlin Li and Dr.-Ing. Hao Luo for many insightful discussions and constructive comments during my studies. From both of them, I have learned a lot on robust and optimal control. I would also like to thank Dr.-Ing. Birgit Kppen-Seliger, Dr.-Ing. Chris Louen, Dr.-Ing. Minjia Krger and Dr.-Ing. Tim Knings for giving me support and valuable advice in supervision of exercises and research projects.

I owe a great debt of gratitude to Dr.-Ing. Zhiwen Chen, Dr.-Ing. Kai Zhang, Dr.-Ing. Yunsong Xu, Dr.-Ing. Lu Qian, who offered enormous help and support during the early days of my life in Duisburg. I am particularly thankful to M.Sc. Micha Obergfell, M.Sc. Yuhong Na, M.Sc. Frederick Hesselmann, M.Sc. Hogir Rafiq, M.Sc. Ting Xue, M.Sc. Deyu Zhang, M.Sc. Reimann Christopher, M.Sc. Caroline Zhu, M.Sc. Yannian Liu, M.Sc. Tieqiang Wang, M.Sc. Jiarui Zhang for making my life in AKS much enjoyable, and for giving me valuable suggestions, generous support and encouragement. I would like to extend my thanks to numerous visiting scholars for all the advice, support, help and the great times. I am also very grateful for the administrative and technical assistance given by Mrs. Sabine Bay, Dipl.-Ing. Klaus Gbel and Mr. Ulrich Janzen.

Lastly, I would like to dedicate this work to my family, to my parents for their unconditional love and care, to my brother for igniting my passion for engineering and all the years of care and support, and especially to my dear wife Xiaodi for being with me all these years with patience and faithful support.

Abbreviations and Notation
Abbreviations
Abbreviation

Description

BLDC

brushless direct current

DP

dynamic programming

ECU

electronic control unit

I/O

input/output

IOR

input and output recovery

KL

Kullback-Leibler

LCF

left coprime factorization

LQG

linear quadratic Gaussian

LQR

linear quadratic regulator

LS

least squares

LTI

linear time-invariant

LTR

loop transfer recovery

MIMO

multiple-input multiple-output

NAC

natural actor-critic

PI

proportional-integral

PID

proportional-integral-derivative

RCF

right coprime factorization

RL

reinforcement learning

SARSA

state-action-reward-state-action

SGD

stochastic gradient descent

SISO

single-input single-output

TD

temporal difference

2-DOF

two-degree-of-freedom

YK

Youla-Kuera

Notation
Notation

Description

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 3

for all

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 4

belong to

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 5

follow

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 6

approximately equal

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 7

not equal

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 8

defined as

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 9

imply

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems - image 10
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