We present a novel approach for solving the probabilistic bounded reachability problem of hybrid systems with parameter uncertainty. Standard approaches to this problem require numerical solutions for large optimization problems, and become unfeasible for systems involving nonlinear dynamics over the reals. Our approach combines randomized sampling of probabilistic system parameters, SMT-based bounded reachability analysis, and statistical tests. We utilize δ-complete decision procedures to solve reachability analysis in a sound way, i.e., we always decide correctly if, for a given combination of parameters, the system actually reaches the unsafe region. Compared to standard simulation-based analysis methods, our approach supports non-deterministic branching, increases the coverage of simulation, and avoids the zero-crossing problem. We demonstrate that our method is feasible for general hybrid systems with parametric uncertainty by applying the implemented tool SReach to various nonlinear hybrid systems with parametric uncertainty.

15 pages

*School of Computing Science, Newcastle University, United Kingdom

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