Feedforward Control in the Presence of Input Nonlinearities

With Application to a Wirebonder

Journal article (2023)

Authors

Maurice Poot Eindhoven University of Technology
Jilles Van Hulst Eindhoven University of Technology
Kai Wa Yan ASMPT Ltd.
Dragan Kostic ASMPT Ltd.
Jim Portegies Eindhoven University of Technology
T.A.E. Oomen Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering , Eindhoven University of Technology
Research Group
Team Jan-Willem van Wingerden (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2023 Maurice Poot, Jilles van Hulst, Kai Wa Yan, Dragan Kostic, Jim Portegies, T.A.E. Oomen
DOI
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https://doi.org/10.1016/j.ifacol.2023.10.1078
Data-driven control Nonlinear system identification Learning for control Identification and control methods Iterative and repetitive learning control
More Info
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Published Date

2023

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Jan-Willem van Wingerden

Abstract

The increasing demands on throughput and accuracy of semiconductor manufacturing equipment necessitates accurate feedforward motion control that includes compensation of input nonlinearities. The aim of this paper is to develop a data-driven feedforward approach consisting of a Wiener feedforward, i.e., linear parameterization with an output nonlinearity, to achieve high tracking accuracy and task flexibility for a class of Hammerstein systems. The developed approach exploits iterative learning control to learn a feedforward signal from data that minimizes the error and utilizes a control-relevant cost function to learn the parameters of a Wiener feedforward parameterization. Experimental validation on a wirebonder shows that the developed approach enables high tracking accuracy and task flexibility.