Resilient Control Under Denial-of-Service and Uncertainty: An Adaptive Dynamic Programming Approach

• 成果类型: Article
• 署名作者: Gao, Weinan; Jiang, Zhong-Ping; Chai, Tianyou
• 署名单位: Northeastern University - China; New York University; New York University Tandon School of Engineering
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3527305
• 发表日期: 2025
• 页码: 4085-4092
• 关键词: Regulation; optimal control; control systems; uncertainty; Closed loop systems; resilience; mathematical models; Denial-of-service attack; Upper bound; vectors; Adaptive dynamic programming (ADP); denial-of-service (DoS) attack; output regulation; resilient optimal control

摘要

In this article, 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.