Iterative Learning Control with Passive Incomplete Information: Algorithms Design and Convergence Analysis

Shen, Dong Dong Shen,

ISBN 10: 9811082669 / ISBN 13: 9789811082665

Published by Springer, 2018

About this Item

Iterative Learning Control with Passive Incomplete Information: Algorithms Design and Convergence Analysis This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

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Bibliographic Details

• Title: Iterative Learning Control with Passive Incomplete Information: Algorithms Design and Convergence Analysis
• Publisher: Springer
• Publication Date: 2018
• Language: English
• ISBN 10: 9811082669
• ISBN 13: 9789811082665
• Binding: hardcover
• Condition: Very Good

About this title

Synopsis

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.

About the Author

Dr. Dong SHEN received the B.S. degree in mathematics from Shandong University, Jinan, China, in 2005. He received the Ph.D. degree in mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), Beijing, China, in 2010.

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.