Abstract
Iterative learning control (ILC) techniques are capable of improving the tracking performance of control systems that repeatedly perform similar tasks by utilizing data from past iterations. The aim of this paper is to design a systematic approach for learning parameterized feedforward signals with limited complexity. The developed method involves an iterative learning control in conjunction with a data-driven sparse subset selection procedure for basis function selection. The ILC algorithm that employs sparse optimization is able to automatically select relevant basis functions and is validated on an industrial flatbed printer.
| Original language | English |
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| Title of host publication | Proceedings of the 2025 American Control Conference, ACC 2025 |
| Publisher | IEEE |
| Pages | 2931-2936 |
| Number of pages | 6 |
| ISBN (Electronic) | 979-8-3315-6937-2 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 2025 American Control Conference, ACC 2025 - Denver, United States Duration: 8 Jul 2025 → 10 Jul 2025 |
Publication series
| Name | Proceedings of the American Control Conference |
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| ISSN (Print) | 0743-1619 |
Conference
| Conference | 2025 American Control Conference, ACC 2025 |
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| Country/Territory | United States |
| City | Denver |
| Period | 8/07/25 → 10/07/25 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-dealsOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.