Name
Abstract | ||
|---|---|---|
This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central problem in data-driven control and reinforcement learning. We propose a method that uses data to directly return a controller without estimating a model of the system. Sufficient conditions are given under which this method returns a stabilizing controller with guaranteed relative error when the data used to design the controller are affected by noise. This method has low complexity as it only requires a finite number of samples of the system response to a sufficiently exciting input, and can be efficiently implemented as a semi-definite programme. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.automatica.2021.109548 | Automatica |
DocType | Volume | Issue |
Journal | 128 | 1 |
ISSN | Citations | PageRank |
0005-1098 | 3 | 0.40 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| de persis | 1 | 1087 | 79.28 |
| Pietro Tesi | 2 | 452 | 32.00 |