Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems

Autor:	Bortolussi, Luca, Cairoli, Francesca, Carbone, Ginevra, Franchina, Francesco, Regolin, Enrico
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.02019 Zobrazit plný text záznamu View this record from Arxiv