Parameter-varying feedforward control
A kernel-based learning approach
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)
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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.