Data-Driven Safety Verification of Stochastic Systems via Barrier Certificates: A Wait-and-Judge Approach
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:441-452, 2022.
We provide a data-driven approach equipped with a formal guarantee for verifying the safety of stochastic systems with unknown dynamics. First, using a notion of barrier certificates, the safety verification for a stochastic system is cast as a robust convex program (RCP). Solving this optimization program is hard because the model of the stochastic system, which is unknown, appears in one of the constraints. Therefore, we construct a scenario convex program (SCP) by collecting a number of samples from trajectories of the system. Then, under some condition over the optimal value of the resulted SCP, we are able to relate its optimal decision variables to the safety of the original stochastic system and provide a formal out-of-sample performance guarantee. Particularly, we propose a so-called wait-and-judge approach which a posteriori checks some condition over the optimal value of the SCP for a fixed number of sampled data. If the condition is satisfied, then the safety specification is satisfied with some probability lower bound and a desired confidence. The effectiveness of our approach in requiring only a low number of samples compared to existing results in the literature is illustrated on a two-tank system by ensuring that the water levels in both tanks never reach a critical zone within a specific time horizon.