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

Jilles Van Hulst, Maurice Poot, Dragan Kostic, Kai Wa Yan, Jim Portegies, Tom Oomen

Research output: Contribution to journalConference articleScientificpeer-review

2 Citations (Scopus)
73 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.

Original languageEnglish
Pages (from-to)235-240
Issue number37
Publication statusPublished - 2022
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: 2 Oct 20225 Oct 2022


  • Applications in semiconductor manufacturing
  • Data-based control
  • Identification for control
  • Iterative learning control
  • Motion Control
  • Nonlinear system identification


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