Parameter-varying feedforward control

A kernel-based learning approach

Journal Article (2025)
Author(s)

Max Van Haren (Eindhoven University of Technology)

Lennart Blanken (Sioux, Eindhoven University of Technology)

Tom Oomen (Eindhoven University of Technology, TU Delft - Team Jan-Willem van Wingerden)

Research Group
Team Jan-Willem van Wingerden
DOI related publication
https://doi.org/10.1016/j.mechatronics.2025.103337
More Info
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Publication Year
2025
Language
English
Research Group
Team Jan-Willem van Wingerden
Volume number
109
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Abstract

The increasing demands for high accuracy in mechatronic systems necessitate the incorporation of parameter variations in feedforward control. The aim of this paper is to develop a data-driven approach for direct learning of parameter-varying feedforward control to increase tracking performance. The developed approach is based on kernel-regularized function estimation in conjunction with iterative learning to directly learn parameter-varying feedforward control from data. This approach enables high tracking performance for feedforward control of linear parameter-varying dynamics, providing flexibility to varying reference tasks. The developed framework is validated on a benchmark industrial experimental setup featuring a belt-driven carriage.