Data-Driven Iterative Learning Control for Discrete-Time Systems
Series: Intelligent Control and Learning Systems; 2;
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Product details:
- Edition number 1st ed. 2022
- Publisher Springer Nature Singapore
- Date of Publication 16 November 2022
- Number of Volumes 1 pieces, Book
- ISBN 9789811959493
- Binding Hardback
- No. of pages235 pages
- Size 235x155 mm
- Weight 586 g
- Language English
- Illustrations X, 235 p. 76 illus., 71 illus. in color. Illustrations, black & white 413
Categories
Long description:
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.
Table of Contents:
Chapter 1: Introduction.- Chapter 2: Iterative Dynamic Linearization of Nonlinear Repetitive Systems
