Abstract
Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.
| Original language | English |
|---|---|
| Article number | 106152 |
| Number of pages | 10 |
| Journal | Control Engineering Practice |
| Volume | 154 |
| DOIs | |
| Publication status | Published - 2025 |
Keywords
- Data-driven control
- Gradient estimation
- Iterative feedback tuning
- MIMO systems