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.
| Original language | English |
|---|---|
| Article number | 103337 |
| Number of pages | 9 |
| Journal | Mechatronics |
| Volume | 109 |
| DOIs | |
| Publication status | Published - Aug 2025 |
Funding
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 .
Keywords
- Feedforward control
- Iterative learning control (ILC)
- Kernel regularization
- Mechatronic systems
- Linear parameter-varying
- Motion control
- Iterative learning control
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