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)

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.

Original languageEnglish
Pages (from-to)235-240
JournalIFAC-PapersOnline
Volume55
Issue number37
DOIs
Publication statusPublished - 2022
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: 2 Oct 20225 Oct 2022

Keywords

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

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