Resilient Learning-Based Control Under Denial-of-Service Attacks
Systems and Control
2024-09-13 v1 Systems and Control
Abstract
In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart.
Cite
@article{arxiv.2409.07766,
title = {Resilient Learning-Based Control Under Denial-of-Service Attacks},
author = {Sayan Chakraborty and Weinan Gao and Kyriakos G. Vamvoudakis and Zhong-Ping Jiang},
journal= {arXiv preprint arXiv:2409.07766},
year = {2024}
}