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<p>This paper presents a comprehensive review and synthesis of recent advances in learning-based resilient control methods for uncertain systems subject to denial-of-service (DoS) attacks. Across discrete-time and continuous-time settings, these frameworks integrate techniques from reinforcement learning (RL), adaptive dynamic programming (ADP), output regulation, switching-systems theory and small-gain analysis to achieve stability and robustness under cyberattacks and model uncertainties. The reviewed works demonstrate that active and data-driven control policies can be learned directly from input-state data, without requiring prior system knowledge, even in the presence of adversarial DoS attacks. Critical DoS attack duration and frequency bounds are characterized to ensure closed-loop stability. Moreover, these bounds are shown to be learnable using input-state data. Together, these advances highlight a unified perspective on resilient control-where learning, robustness, and security are jointly addressed to guarantee stable performance of cyber-physical systems under adverse network conditions.</p>

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A Review on Resilient Reinforcement Learning for Linear Cyber-Physical Systems

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Sayan Chakraborty,
  • Weinan Gao,
  • Zhong-Ping Jiang

摘要

This paper presents a comprehensive review and synthesis of recent advances in learning-based resilient control methods for uncertain systems subject to denial-of-service (DoS) attacks. Across discrete-time and continuous-time settings, these frameworks integrate techniques from reinforcement learning (RL), adaptive dynamic programming (ADP), output regulation, switching-systems theory and small-gain analysis to achieve stability and robustness under cyberattacks and model uncertainties. The reviewed works demonstrate that active and data-driven control policies can be learned directly from input-state data, without requiring prior system knowledge, even in the presence of adversarial DoS attacks. Critical DoS attack duration and frequency bounds are characterized to ensure closed-loop stability. Moreover, these bounds are shown to be learnable using input-state data. Together, these advances highlight a unified perspective on resilient control-where learning, robustness, and security are jointly addressed to guarantee stable performance of cyber-physical systems under adverse network conditions.