Name
Abstract | ||
|---|---|---|
Iterative feedback tuning (IFT) enables the data-driven tuning of controller parameters without the explicit need for a parametric model. It is known, however, that IFT can lead to nonrobust solutions. The aim of this paper is to develop an IFT approach with robustness constraints. A constrained IFT problem is formulated that is solved by introducing a penalty function. Essentially, the gradient estimates decompose into: 1) the well-known IFT gradients and 2) the gradients with respect to this penalty function. The latter are obtained through a nonparametric model of the controlled system. This guarantees robust stability while only requiring a nonparametric model. The experimental results obtained from the motion control systems of an industrial wafer scanner confirm enhanced performance with guaranteed robustness estimates. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TCST.2015.2418311 | Control Systems Technology, IEEE Transactions |
Keywords | Field | DocType |
Robustness,Tuning,Optimization,Frequency-domain analysis,Context,Feedforward neural networks,Semiconductor device modeling | Frequency domain,Control theory,Feedforward neural network,Motion control,Parametric model,Control theory,Control engineering,Robustness (computer science),Robust control,Mathematics,Penalty method | Journal |
Volume | Issue | ISSN |
PP | 99 | 1063-6536 |
Citations | PageRank | References |
6 | 0.50 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcel François Heertjes | 1 | 12 | 1.79 |
| Bart Van der Velden | 2 | 6 | 0.50 |
| Oomen, T. | 3 | 95 | 17.42 |