Abstract
Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration-and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.
Original language | English |
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Title of host publication | Proceedings of the IEEE 61st Conference on Decision and Control (CDC 2022) |
Publisher | IEEE |
Pages | 1485-1490 |
ISBN (Print) | 978-1-6654-6761-2 |
DOIs | |
Publication status | Published - 2022 |
Event | IEEE 61st Conference on Decision and Control (CDC 2022) - Cancún, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 |
Conference
Conference | IEEE 61st Conference on Decision and Control (CDC 2022) |
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Country/Territory | Mexico |
City | Cancún |
Period | 6/12/22 → 9/12/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise 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.
Keywords
- Simulation
- Computational modeling
- Aerospace electronics
- Manufacturing
- Computational efficiency
- Printers
- Complex systems