Work

Parameter-varying feedforward control: A kernel-based learning approach

Year: 2025

Type: article

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.

Source: Mechatronics

DOI: 10.1016/j.mechatronics.2025.103337

Authors: Max van Haren, Lennart Blanken, Tom Oomen

Institutions: Eindhoven University of Technology, Delft University of Technology

Language: English

Topic: Iterative Learning Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,940