Abstract
The increasing demands on throughput and accuracy of semiconductor manufacturing equipment necessitates accurate feedforward motion control that includes compensation of input nonlinearities. The aim of this paper is to develop a data-driven feedforward approach consisting of a Wiener feedforward, i.e., linear parameterization with an output nonlinearity, to achieve high tracking accuracy and task flexibility for a class of Hammerstein systems. The developed approach exploits iterative learning control to learn a feedforward signal from data that minimizes the error and utilizes a control-relevant cost function to learn the parameters of a Wiener feedforward parameterization. Experimental validation on a wirebonder shows that the developed approach enables high tracking accuracy and task flexibility.
| Original language | English |
|---|---|
| Pages (from-to) | 1895-1900 |
| Number of pages | 6 |
| Journal | IFAC-PapersOnLine |
| Volume | 56 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Jul 2023 |
| Event | 22nd World Congress of the International Federation of Automatic Control (IFAC 2023 World Congress) - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023 Conference number: 22 https://www.ifac2023.org/ |
Funding
The authors would like to thank Robin van Es for his contributions to this research.
Keywords
- Data-driven control
- Identification and control methods
- Iterative and repetitive learning control
- Learning for control
- Nonlinear system identification