Samenvatting
Feedforward control is essential for achieving high accuracy for nonlinear mechatronic systems. The aim of this paper is to develop a kernel-based iterative learning control (ILC) approach that enables the specification of parameters through suitable kernels. The developed kernel-based iterative learning control (KILC) framework employs basis functions to facilitate task flexibility and nonlinear and non-causal feedforward as function of the reference signal. Experimental results on a printer motion system subject to nonlinear friction demonstrate that the developed framework is capable of achieving improved performance for systems with non-minimum phase and higher-order dynamics compared to preexisting feedforward methods.
Originele taal-2 | Engels |
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Titel | 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Pagina's | 1052-1057 |
Aantal pagina's | 6 |
ISBN van elektronische versie | 9781665441391 |
DOI's | |
Status | Gepubliceerd - 24 aug. 2021 |
Evenement | 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021 - Delft, Nederland Duur: 12 jul. 2021 → 16 jul. 2021 |
Congres
Congres | 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021 |
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Land/Regio | Nederland |
Stad | Delft |
Periode | 12/07/21 → 16/07/21 |
Bibliografische nota
Publisher Copyright:© 2021 IEEE.
Financiering
The work is supported by ASM Pacific Technology, Beuningen, The Netherlands.