Parameter-Varying Feedforward Control: A Kernel-Based Learning Approach

Max van Haren; Lennart Blanken; Tom Oomen

doi:10.1016/j.mechatronics.2025.103337

Parameter-Varying Feedforward Control: A Kernel-Based Learning Approach

Max van Haren (Corresponding author), Lennart Blanken, Tom Oomen

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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.

Originele taal-2	Engels
Artikelnummer	103337
Aantal pagina's	9
Tijdschrift	Mechatronics
Volume	109
DOI's	https://doi.org/10.1016/j.mechatronics.2025.103337
Status	Gepubliceerd - aug. 2025

Financiering

This work is part of the research programme VIDI with project number 15698, which is (partly) financed by, The Netherlands Organisation for Scientific Research (NWO) . This research has received funding from the ECSEL Joint Undertaking under grant agreement 101007311 (IMOCO4.E). The Joint Undertaking receives support from the European Union Horizon 2020 research and innovation programme .

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10.1016/j.mechatronics.2025.103337Licentie: CC BY

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title = "Parameter-Varying Feedforward Control: A Kernel-Based Learning Approach",

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.",

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year = "2025",

month = aug,

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Parameter-Varying Feedforward Control: A Kernel-Based Learning Approach

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