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Data-driven iterative learning control for discrete-time systems
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 dat...
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| Main Author: | |
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| Corporate Author: | |
| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
2022.
Singapore : 2022. |
| Series: | Intelligent control and learning systems ;
v. 2. |
| Physical Description: |
1 online resource (239 pages). |
| Subjects: | |
| Online Access: | SpringerLink - Click here for access |
| Summary: | 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 systems 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. |
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| Physical Description: |
1 online resource (239 pages). |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 9789811959509 9811959501 |