In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty.
@article{arxiv.2411.06689,
title = {Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach},
author = {Weinan Gao and Zhong-Ping Jiang and Tianyou Chai},
journal= {arXiv preprint arXiv:2411.06689},
year = {2024}
}