Iterative Learning Control for Systems with Iteration-Varying Trial Lengths: Synthesis and Analysis

ISBN-13: 9789811361357 / Angielski / Twarda / 2019 / 256 str.

Dong Shen; Xuefang Li

Spis treści

Introduction.- Averaging and Expectation Techniques for Linear DT Systems.- Lifting Techniques for Linear DT Systems.- Moving Averaging Techniques for Linear DT Systems.- Switching System Techniques for Linear DT Systems.- Moving Averaging Tehcniques for Nonlinear CT Systems.- Modified Lambda-Norm Techniques for Nonlinear DT Systems.- Sampled-Data Control Techniques for Nonlinear CT Systems.- CEF Techniques for Parameterized Nonlinear CT Systems.- CEF Techniques for Nonparameterized Nonlinear CT Systems.- Summary and Future Research Directions.- Appendix.

O autorze

Dong SHEN received his B.S. degree in mathematics from Shandong University, Jinan, China, in 2005, and his 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, he was a postdoctoral fellow at the Institute of Automation, CAS, and since 2012 he has been with the College of Information Science and Technology, Beijing University of Chemical Technology (BUCT), Beijing, China, where he now is a professor. He was a visiting scholar at the National University of Singapore from 2016 to 2017. His current research interests include iterative learning control, stochastic control and optimization. He has published more than 60 refereed journal and conference papers, and is the author of Iterative Learning Control with Passive Incomplete Information (Springer, 2018) and 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. 

Xuefang LI received her B.Sc. and M.Sc. degrees in mathematics from Sichuan University, Chengdu, China, in 2009 and 2012, respectively. She received her Ph.D. degree in control engineering from the National University of Singapore, Singapore, 2016. In 2016 she joined Imperial College London as a research associate and is currently working on control and energy management of hybrid electric vehicles. Her research interests include learning control and its applications, robotic motion control, and optimal control of hybrid electric vehicles. She has published more than 30 refereed journal and conference papers. In addition, she is a co-author of Iterative Learning Control for Multi-Agent Systems Coordination (Wiley, 2017). Dr. Li was the recipient of the ICCAS outstanding paper award in 2013, the President’s Graduate Fellowships of Singapore in 2014 and the Chinese Government Award for Outstanding Students Abroad in 2015.

Blurb

This book presents a comprehensive and detailed study on iterative learning control (ILC) for systems with iteration-varying trial lengths. Instead of traditional ILC, which requires systems to repeat on a fixed time interval, this book focuses on a more practical case where the trial length might randomly vary from iteration to iteration. The iteration-varying trial lengths may be different from the desired trial length, which can cause redundancy or dropouts of control information in ILC, making ILC design a challenging problem. The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator. It not only discusses recent advances in ILC for systems with iteration-varying trial lengths, but also includes numerous intuitive figures to allow readers to develop an in-depth understanding of the intrinsic relationship between the incomplete information environment and the essential tracking performance. This book is intended for academic scholars and engineers who are interested in learning about control, data-driven control, networked control systems, and related fields. It is also a useful resource for graduate students in the above field.