Zhou Yuanqiang, Vamvoudakis Kyriakos G, Haddad Wassim M, Jiang Zhong-Ping
IEEE Trans Cybern. 2021 Sep;51(9):4648-4660. doi: 10.1109/TCYB.2020.3006871. Epub 2021 Sep 15.
In this article, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor and actuator attacks. Specifically, we use a bank of observer-based estimators to detect the attacks while introducing a threat-detection level function. Under nominal conditions, the system operates with a nominal-feedback controller with the developed attack monitoring process checking the reliance of the measurements. If there exists an attacker injecting attack signals to a subset of the sensors and/or actuators, then the attack mitigation process is triggered and a two-player, zero-sum differential game is formulated with the defender being the minimizer and the attacker being the maximizer. Next, we solve the underlying joint state estimation and attack mitigation problem and learn the secure control policy using a reinforcement-learning-based algorithm. Finally, two illustrative numerical examples are provided to show the efficacy of the proposed framework.
在本文中，我们针对存在传感器和执行器攻击情况下的信息物理系统，开发了一种基于学习的安全控制框架。具体而言，我们使用一组基于观测器的估计器来检测攻击，同时引入一个威胁检测水平函数。在标称条件下，系统通过标称反馈控制器运行，所开发的攻击监测过程会检查测量的可靠性。如果存在攻击者向一部分传感器和/或执行器注入攻击信号，那么将触发攻击缓解过程，并制定一个两人零和微分博弈，其中防御者为最小化者，攻击者为最大化者。接下来，我们解决潜在的联合状态估计和攻击缓解问题，并使用基于强化学习的算法学习安全控制策略。最后，提供了两个说明性数值示例以展示所提出框架的有效性。
