A Review on Resilient Reinforcement Learning for Linear Cyber-Physical Systems
简介：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.展开
学者：CHAKRABORTYSayanGaoWeinanJIANGZhong-Ping
关键词：cyber-physical systemcybersecuritylearning-based controlresilient reinforcement learn-ing
在线出版日期：2026-05-14 （网站首发日期）
