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arXiv · 2411.06689

Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach

Weinan Gao·Zhong-Ping Jiang·Tianyou Chai
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Abstract

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.

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Weinan Gao, Zhong-Ping Jiang, Tianyou Chai. 2024-11-11. Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach. https://arxiv.org/abs/2411.06689

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