Reachability analysis of nonlinear discrete-time systems: a data-driven approach
Giuseppe Franzè, Domenico Famularo, Francesco Tedesco, Vicenç Puig · 2025
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 inner and outer approximations of the exact predecessor and successor sets, whose accuracy is evaluated by means of statistical tests. Finally, the proposed approach is assessed by resorting to a benchmark example and providing numerical comparisons with a model-based competitor.