Iterative Learning Control for Systems with Iteration-Varying Trial Lengths (PDF)
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...
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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 numerousintuitive 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.
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
- Autoren: Dong Shen , Xuefang Li
- 2019, 1st ed. 2019, 256 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 9811361363
- ISBN-13: 9789811361364
- Erscheinungsdatum: 29.01.2019
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- Dateiformat: PDF
- Größe: 8.83 MB
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