Feedforward Control in the Presence of Input Nonlinearities

A Learning-based Approach

Journal article (2022)

Authors

Jilles Van Hulst Eindhoven University of Technology
Maurice Poot Eindhoven University of Technology
Dragan Kostic ASM Pacific Technology
Kai Wa Yan ASM Pacific Technology
Jim Portegies Eindhoven University of Technology
T.A.E. Oomen Eindhoven University of Technology, Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Jilles Van Hulst, Maurice Poot, Dragan Kostic, Kai Wa Yan, Jim Portegies, T.A.E. Oomen
DOI
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https://doi.org/10.1016/j.ifacol.2022.11.190
Motion Control Iterative learning control Identification for control Nonlinear system identification Applications in semiconductor manufacturing Data-based control
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Published Date

2022

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

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.