Adaptive Secure Control for Uncertain Cyber-Physical Systems With Markov Switching Against Both Sensor and Actuator Attacks
针对同时遭受传感器和执行器隐蔽攻击、且转移速率未知的不确定信息物理系统，提出基于神经网络的滑模自适应安全控制器，保证系统随机稳定，并用F-404航空发动机模型验证了有效性。
In this article, adaptive secure controller synthesis for uncertain cyber-physical systems with Markov switching (CPSMSs), both sensor and actuator stealthy attacks as well as generally unknown transition rates (GUTRs), is under consideration via neural sliding mode control (SMC) technique. In order to resist unknown attack signals from both sensor and actuator channels, a novel neural network (NN)-based SMC design is performed, which could not only guarantee the boundedness of relevant adaptive data but also force the actual state trajectories to arrive at the proposed sliding mode surface (SMS) with limited moments almost surely. Then, a fresh stochastically stable criterion for the resultant plant is provided in spite of hidden cyber attacks, GUTRs, and structural uncertainty, relying on the arrival of the SMS and stochastic stability theory. Finally, an F-404 aircraft engine model with performance comparisons is offered to confirm the feasibleness of the theoretical result.