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.
@article{arxiv.2209.11504,
title = {Feedforward Control in the Presence of Input Nonlinearities: A Learning-based Approach},
author = {Jilles van Hulst and Maurice Poot and Dragan Kostić and Kai Wa Yan and Jim Portegies and Tom Oomen},
journal= {arXiv preprint arXiv:2209.11504},
year = {2023}
}
Comments
6 pages, 8 figures, to be presented at Modeling Estimation and Control Conference 2022 in Jersey City, NJ, to be published in IFAC-PapersOnLine