Name
Abstract | ||
|---|---|---|
We address the problem of designing a stabilizing closed-loop control law directly from input and state measurements collected in an experiment. In the presence of a process disturbance in data, we have that a set of dynamics could have generated the collected data and we need the designed controller to stabilize such set of data-consistent dynamics robustly. For this problem of data-driven control with noisy data, we advocate the use of a popular tool from robust control, Petersen’s lemma. In the cases of data generated by linear and polynomial systems, we conveniently express the uncertainty captured in the set of data-consistent dynamics through a matrix ellipsoid, and we show that a specific form of this matrix ellipsoid makes it possible to apply Petersen’s lemma to all of the mentioned cases. In this way, we obtain necessary and sufficient conditions for data-driven stabilization of linear systems through a linear matrix inequality. The matrix ellipsoid representation enables insights and interpretations of the designed control laws. In the same way, we also obtain sufficient conditions for data-driven stabilization of polynomial systems through alternate (convex) sum-of-squares programs. The findings are illustrated numerically. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.automatica.2022.110537 | Automatica |
Keywords | DocType | Volume |
Data-based control,Optimization-based controller synthesis,Analysis of systems with uncertainty,Robust control of nonlinear systems,Linear matrix inequalities,Sum-of-squares | Journal | 145 |
Issue | ISSN | Citations |
1 | 0005-1098 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Bisoffi | 1 | 0 | 0.34 |
| de persis | 2 | 1087 | 79.28 |
| Pietro Tesi | 3 | 452 | 32.00 |