Abstract
Repetitive control can lead to high performance by attenuating periodic disturbances completely, yet it may amplify non-periodic disturbances. The aim of this paper is to achieve both fast learning and low errors in repetitive control. To this end, a nonlinear learning filter is introduced that distinguishes between periodic and non-periodic errors based on their typical amplitude characteristics to adapt the extent to which they are included in the repetitive controller. Convergence conditions for the nonlinear repetitive control system are derived by casting the resulting closed-loop as a discrete-time convergent system. Simulation results of the proposed approach demonstrate fast learning and small errors.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023) |
| Publisher | IEEE |
| Pages | 2891-2896 |
| Number of pages | 6 |
| ISBN (Electronic) | 979-8-3503-0124-3 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| ISSN (Print) | 0743-1546 |
| ISSN (Electronic) | 2576-2370 |
Conference
| Conference | 62nd IEEE Conference on Decision and Control, CDC 2023 |
|---|---|
| Country/Territory | Singapore |
| City | Singapore |
| Period | 13/12/23 → 15/12/23 |
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.