2026-04-05T10:52:12Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4595262026-03-31T12:58:00Zcom_2072_1033col_2072_452950

Data-driven forward reachability analysis Franzè, Giuseppe Puig Cayuela, Vicenç Àrees temàtiques de la UPC::Informàtica::Enginyeria del software Linear systems Data-driven modeling Computer aided software engineering Automation Accuracy Reinforcement learning Benchmark testing Approximation algorithms Reachability analysis Nonlinear systems © 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 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. This work was supported by the research project - ID:20222N4C8E “Resilient and Secure Networked Multivehicle Systems in Adversary Environments” granted by the European Union - Next Generation EU, Mission 4, Component 1, CUP H53D230004100. Peer Reviewed Postprint (author's final draft) 2025 Conference report https://ieeexplore.ieee.org/document/11164032 Open Access