Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems
Systems and Control
2021-03-29 v2 Systems and Control
Abstract
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.
Cite
@article{arxiv.2009.02019,
title = {Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems},
author = {Luca Bortolussi and Francesca Cairoli and Ginevra Carbone and Francesco Franchina and Enrico Regolin},
journal= {arXiv preprint arXiv:2009.02019},
year = {2021}
}