Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach
Abstract
In this paper, we provide a direct data-driven approach to synthesize safety controllers for unknown linear systems affected by unknown-but-bounded disturbances, in which identifying the unknown model is not required. First, we propose a notion of $\gamma$-robust safety invariant ($\gamma$-RSI) sets and their associated state-feedback controllers, which can be applied to enforce invariance properties. Then, we formulate a data-driven computation of these sets in terms of convex optimization problems with linear matrix inequalities (LMI) as constraints, which can be solved based on a finite number of data collected from a single input-state trajectory of the system. To show the effectiveness of the proposed approach, we apply our results to a 4-dimensional inverted pendulum.
- Publication:
-
arXiv e-prints
- Pub Date:
- June 2022
- DOI:
- 10.48550/arXiv.2206.00354
- arXiv:
- arXiv:2206.00354
- Bibcode:
- 2022arXiv220600354Z
- Keywords:
-
- Electrical Engineering and Systems Science - Systems and Control
- E-Print:
- 6th IEEE Conference on Control Technology and Applications