Overview
- Presents a comprehensive discussion of iterative learning control (ILC) in various data dropout environments
- Proposes several systematic procedures for the design and analysis of ILC for stochastic systems with passive incomplete information
- Details the current research on stochastic ILC and the major research techniques used
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Table of contents (14 chapters)
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Front Matter
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One-Side Data Dropout
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Front Matter
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Two-Side Data Dropout
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Front Matter
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General Incomplete Information Conditions
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Front Matter
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Back Matter
Keywords
About this book
This book presents an in-depth discussion of iterative learning control (ILC) with passive incomplete information, highlighting the incomplete input and output data resulting from practical factors such as data dropout, transmission disorder, communication delay, etc.—a cutting-edge topic in connection with the practical applications of ILC.
It describes in detail three data dropout models: the random sequence model, Bernoulli variable model, and Markov chain model—for both linear and nonlinear stochastic systems. Further, it proposes and analyzes two major compensation algorithms for the incomplete data, namely, the intermittent update algorithm and successive update algorithm. Incomplete information environments include random data dropout, random communication delay, random iteration-varying lengths, and other communication constraints.
With numerous intuitive figures to make the content more accessible, the book explores several potential solutions to this topic, ensuring that readers are not only introduced to the latest advances in ILC for systems with random factors, but also gain an in-depth understanding of the intrinsic relationship between incomplete information environments and essential tracking performance. It is a valuable resource for academics and engineers, as well as graduate students who are interested in learning about control, data-driven control, networked control systems, and related fields.Authors and Affiliations
About the author
From 2010 to 2012, Dr. Shen was a Post-Doctoral Fellow with the Institute of Automation, CAS. Since 2012, he has been an Associate Professor with College of Information Science and Technology, Beijing University of Chemical Technology (BUCT), Beijing, China. He was a visiting scholar at National University of Singapore from 2016 to 2017.
Dr. Shen's current research interests include iterative learning control, stochastic control and optimization. He has published more than 50 refereed journal and conference papers. He is the author of Stochastic Iterative Learning Control (Science Press, 2016, in Chinese), co-author of Iterative Learning Control for Multi-Agent Systems Coordination (Wiley, 2017), and co-editor of Service Science, Management and Engineering: Theory and Applications (Academic Press and Zhejiang University Press, 2012). Dr. Shen received the IEEE CSS Beijing Chapter Young Author Prize in 2014 and the Wentsun Wu Artificial Intelligence Science and Technology Progress Award in 2012.
Bibliographic Information
Book Title: Iterative Learning Control with Passive Incomplete Information
Book Subtitle: Algorithms Design and Convergence Analysis
Authors: Dong Shen
DOI: https://doi.org/10.1007/978-981-10-8267-2
Publisher: Springer Singapore
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2018
Hardcover ISBN: 978-981-10-8266-5Published: 26 April 2018
Softcover ISBN: 978-981-13-4105-2Published: 25 December 2018
eBook ISBN: 978-981-10-8267-2Published: 16 April 2018
Edition Number: 1
Number of Pages: XIV, 294
Number of Illustrations: 89 b/w illustrations
Topics: Control and Systems Theory, Vibration, Dynamical Systems, Control, Mathematical and Computational Engineering