Feedforward control in the presence of input nonlinearities: A Learning-based approach

Onderzoeksoutput: Bijdrage aan tijdschriftCongresartikelpeer review

1 Citaat (Scopus)
42 Downloads (Pure)


Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.
Originele taal-2Engels
Pagina's (van-tot)235-240
Aantal pagina's6
Nummer van het tijdschrift37
StatusGepubliceerd - 31 okt. 2022
Evenement2022 Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, Verenigde Staten van Amerika
Duur: 2 okt. 20225 okt. 2022


Duik in de onderzoeksthema's van 'Feedforward control in the presence of input nonlinearities: A Learning-based approach'. Samen vormen ze een unieke vingerafdruk.

Citeer dit