Abstract
Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC is achieved by rewriting the nonlinear terms in the control scheme as a function of the previous feedforward parameters. A simulation study on an multivariable industrial printer shows that the developed framework converges and achieves significant better performance than direct application of the RBF algorithm using SK-iterations for SISO systems.
| Original language | English |
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| Title of host publication | Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023) |
| Publisher | IEEE |
| Pages | 4644-4649 |
| 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.