Abstract
Nonlinear iterative learning control (ILC) and nonlinear repetitive control (RC) approaches introduce additional design freedom compared to linear time-invariant (LTI) approaches. Since the actual performance improvements depend on the parameters used in the nonlinearity, the aim of this paper is to optimize these parameters during the learning process. With optimal parameters, the nonlinear algorithms can outperform their LTI counterparts, for example by achieving fast attenuation of repeating disturbances without amplifying non-repeating disturbances. In this paper, we present the algorithm for the automatic learning/tuning process and validate it using simulations of an industrial flatbed printer.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IEEE 64th Conference on Decision and Control (CDC 2025) |
| Publisher | IEEE |
| Pages | 765-770 |
| Number of pages | 6 |
| ISBN (Electronic) | 979-8-3315-2627-6 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 64th Conference on Decision and Control (CDC 2025) - Rio de Janeiro, Brazil Duration: 9 Dec 2025 → 12 Dec 2025 |
Conference
| Conference | 64th Conference on Decision and Control (CDC 2025) |
|---|---|
| Country/Territory | Brazil |
| City | Rio de Janeiro |
| Period | 9/12/25 → 12/12/25 |
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.