Samenvatting
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.
Originele taal-2 | Engels |
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Titel | 2023 62nd IEEE Conference on Decision and Control, CDC 2023 |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Pagina's | 4644-4649 |
Aantal pagina's | 6 |
ISBN van elektronische versie | 979-8-3503-0124-3 |
DOI's | |
Status | Gepubliceerd - 19 jan. 2024 |
Evenement | 2023 62nd IEEE Conference on Decision and Control (CDC) - Singapore, Singapore Duur: 13 dec. 2023 → 15 dec. 2023 Congresnummer: 62 |
Congres
Congres | 2023 62nd IEEE Conference on Decision and Control (CDC) |
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Verkorte titel | CDC 2023 |
Land/Regio | Singapore |
Stad | Singapore |
Periode | 13/12/23 → 15/12/23 |