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Optimal iterative learning control =...

Chu, Bing.

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Optimal iterative learning control = a practitioner's guide /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal iterative learning control/ by Bing Chu, David H. Owens.
其他題名:	a practitioner's guide /
作者:	Chu, Bing.
其他作者:	Owens, D. H.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xx, 356 p. :ill. (some col.), digital ;24 cm.
內容註:	1. Introduction to Iterative Learning Control -- 2. Brief Review of Systems Control Theory -- 3. Parameter Optimal Iterative Learning Control -- 4. Inverse Based Iterative Learning Control -- 5. Gradient Based Iterative Learning Control -- 6. Norm Optimal Iterative Learning Control -- 7. Optimal Iterative Learning Control: Constraint Handling -- 8. Accelerating the Convergence -- 9. A Case Study on a Robotic Testing Platform -- 10. Summary and Future Research Directions.
Contained By:	Springer Nature eBook
標題:	Intelligent control systems. -
電子資源:	https://doi.org/10.1007/978-3-031-80236-2
ISBN:	9783031802362

Optimal iterative learning control = a practitioner's guide /
Chu, Bing.

Optimal iterative learning controla practitioner's guide /[electronic resource] :by Bing Chu, David H. Owens. - Cham :Springer Nature Switzerland :2025. - xx, 356 p. :ill. (some col.), digital ;24 cm. - Advances in industrial control,2193-1577. - Advances in industrial control..

1. Introduction to Iterative Learning Control -- 2. Brief Review of Systems Control Theory -- 3. Parameter Optimal Iterative Learning Control -- 4. Inverse Based Iterative Learning Control -- 5. Gradient Based Iterative Learning Control -- 6. Norm Optimal Iterative Learning Control -- 7. Optimal Iterative Learning Control: Constraint Handling -- 8. Accelerating the Convergence -- 9. A Case Study on a Robotic Testing Platform -- 10. Summary and Future Research Directions.

This book introduces an optimal iterative learning control (ILC) design framework from the end user's point of view. Its central theme is the understanding of model dynamics, the construction of a procedure for systematic input updating and their contribution to successful algorithm design. The authors discuss the many applications of ILC in industrial systems, applications such as robotics and mechanical testing. The text covers a number of optimal ILC design methods, including gradient-based and norm-optimal ILC. Their convergence properties are described and detailed design guidelines, including performance-improvement mechanisms, are presented. Readers are given a clear picture of the nature of ILC and the benefits of the optimization-based approach from the conceptual and mathematical foundations of the problem of algorithm construction to the impact of available parameters in making acceleration of algorithmic convergence possible. Three case studies on robotic platforms, an electro-mechanical machine, and robot-assisted stroke rehabilitation are included to demonstrate the application of these methods in the real-world. With its emphasis on basic concepts, detailed design guidelines and examples of benefits, Optimal Iterative Learning Control will be of value to practising engineers and academic researchers alike.

ISBN: 9783031802362

Standard No.: 10.1007/978-3-031-80236-2doiSubjects--Topical Terms:

546464
Intelligent control systems.

LC Class. No.: TJ217.5

Dewey Class. No.: 658.4038

Optimal iterative learning control = a practitioner's guide /
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505 0 $a 1. Introduction to Iterative Learning Control -- 2. Brief Review of Systems Control Theory -- 3. Parameter Optimal Iterative Learning Control -- 4. Inverse Based Iterative Learning Control -- 5. Gradient Based Iterative Learning Control -- 6. Norm Optimal Iterative Learning Control -- 7. Optimal Iterative Learning Control: Constraint Handling -- 8. Accelerating the Convergence -- 9. A Case Study on a Robotic Testing Platform -- 10. Summary and Future Research Directions.
520 $a This book introduces an optimal iterative learning control (ILC) design framework from the end user's point of view. Its central theme is the understanding of model dynamics, the construction of a procedure for systematic input updating and their contribution to successful algorithm design. The authors discuss the many applications of ILC in industrial systems, applications such as robotics and mechanical testing. The text covers a number of optimal ILC design methods, including gradient-based and norm-optimal ILC. Their convergence properties are described and detailed design guidelines, including performance-improvement mechanisms, are presented. Readers are given a clear picture of the nature of ILC and the benefits of the optimization-based approach from the conceptual and mathematical foundations of the problem of algorithm construction to the impact of available parameters in making acceleration of algorithmic convergence possible. Three case studies on robotic platforms, an electro-mechanical machine, and robot-assisted stroke rehabilitation are included to demonstrate the application of these methods in the real-world. With its emphasis on basic concepts, detailed design guidelines and examples of benefits, Optimal Iterative Learning Control will be of value to practising engineers and academic researchers alike.
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