Abstract
Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input.
Original language | English |
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Pages (from-to) | 235-240 |
Journal | IFAC-PapersOnline |
Volume | 55 |
Issue number | 37 |
DOIs | |
Publication status | Published - 2022 |
Event | 2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States Duration: 2 Oct 2022 → 5 Oct 2022 |
Keywords
- Applications in semiconductor manufacturing
- Data-based control
- Identification for control
- Iterative learning control
- Motion Control
- Nonlinear system identification