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＜E-Book＞
Data-Driven Iterative Learning Control for Discrete-Time Systems / by Ronghu Chi, Yu Hui, Zhongsheng Hou
(Intelligent Control and Learning Systems. ISSN:26625466 ; 2)

Edition	1st ed. 2022.
Publisher	(Singapore : Springer Nature Singapore : Imprint: Springer)
Year	2022
Language	English
Size	X, 235 p. 76 illus., 71 illus. in color : online resource
Authors	*Chi, Ronghu author Hui, Yu author Hou, Zhongsheng author SpringerLink (Online service)
Subjects	LCSH:Control engineering LCSH:Stochastic processes LCSH:Mathematics -- Data processing  All Subject Search FREE:Control and Systems Theory FREE:Stochastic Systems and Control FREE:Computational Science and Engineering
Notes	Chapter 1: Introduction -- Chapter 2: Iterative Dynamic Linearization of Nonlinear Repetitive Systems -- Chapter 3: Data-Driven Optimal Iterative Learning Control -- Chapter 4: Knowledge Enhanced Data-Driven Optimal Terminal ILC -- Chapter 5: Data-Driven Optimal Point-to-Point ILC using Intermidient Information -- Chapter 6: Higher order Data-Driven Optimal Iterative Learning Control -- Chapter 7: Data-Driven Optimal Iterative Learning Control with Varying Trial Length -- Chapter 8: Data-Driven Optimal Iterative Learning Control with Package Dropouts -- Chapter 9: Constrained Data-Driven Optimal Iterative Learning Control -- Chapter 10: ESO-based Data-Driven Optimal Iterative Learning Control -- Chapter 11: Quantized Data-Driven Optimal Iterative Learning Control -- Chapter 12: Event-triggered Data-driven Optimal Iterative Learning Control -- Chapter 13: Conclusions and Perspectives -- Appendices This book belongs to the subject of control and systems theory. It studies a novel data-driven framework for the design and analysis of iterative learning control (ILC) for nonlinear discrete-time systems. A series of iterative dynamic linearization methods is discussed firstly to build a linear data mapping with respect of the system’s output and input between two consecutive iterations. On this basis, this work presents a series of data-driven ILC (DDILC) approaches with rigorous analysis. After that, this work also conducts significant extensions to the cases with incomplete data information, specified point tracking, higher order law, system constraint, nonrepetitive uncertainty, and event-triggered strategy to facilitate the real applications. The readers can learn the recent progress on DDILC for complex systems in practical applications. This book is intended for academic scholars, engineers, and graduate students who are interested in learning control, adaptive control, nonlinear systems, and related fields HTTP:URL=https://doi.org/10.1007/978-981-19-5950-9

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