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arXiv · 2209.11504

Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach

Jilles van Hulst·Maurice Poot·Dragan Kostić·Kai Wa Yan·Jim Portegies·Tom Oomen
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Abstract

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.

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Jilles van Hulst, Maurice Poot, Dragan Kostić, Kai Wa Yan, Jim Portegies, Tom Oomen. 2022-09-23. Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach. https://doi.org/10.1016/j.ifacol.2022.11.190

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