Name
Abstract | ||
|---|---|---|
This paper studies worst-case robust optimal tracking using noisy input-output data. We utilize behavioral system theory to represent system trajectories, while avoiding explicit system identification. We assume that the recent output data used in the data-dependent representation are noisy and we provide a non-conservative design procedure for robust control based on optimization with a linear cost and LMI constraints. Our methods rely on the parameterization of noise sequences compatible with the data-dependent system representation and on a suitable reformulation of the performance specification, which further enable the application of the S-lemma to derive an LMI optimization problem. The performance of the new controller is discussed through simulations. |
Year | Venue | DocType |
|---|---|---|
2021 | L4DC | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Xu | 1 | 10 | 7.96 |
| Mustafa S. Turan | 2 | 0 | 1.69 |
| Baiwei Guo | 3 | 0 | 1.01 |
| Giancarlo Ferrari-Trecate | 4 | 831 | 77.29 |