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arXiv · 2502.21105

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

Max van Haren·Lennart Blanken·Tom Oomen
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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.

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Max van Haren, Lennart Blanken, Tom Oomen. 2025-02-28. Parameter-Varying Feedforward Control: A Kernel-Based Learning Approach. https://doi.org/10.1016/j.mechatronics.2025.103337

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