Data-driven forward reachability analysis

In this paper, the reachability analysis for a class of nonlinear systems is addressed by resorting to a data-driven setting. The resulting approach combines into a unique framework linear time-invariant system behavior, data-driven modeling and reinforcement learning algorithms. This allows to determine outer approximations of the exact successor sets whose accuracy is evaluated by means of statistical tests. Finally, the validity of the proposed approach is tested by resorting to a benchmark example and providing numerical comparisons with a well-reputed competitor.