Model-Free Output Regulation of Networked Systems Under Unknown Hybrid Attacks
研究了在传感器-控制器和控制器-执行器通道上遭受拒绝服务、重放和欺骗攻击的未知离散时间系统的输出调节问题，提出了一种基于学习的滚动时域控制方法，无需系统模型和直接测量外部扰动，能抵消混合攻击的不利影响。
This article considers the output regulation problem for an unknown discrete-time system subject to the random combination of denial-of-service, replay, and deception attacks on both sensor-controller and controller-actuator channels. We propose a learning-based receding-horizon control with historical output signals. It offers two advantages over state and output feedback regulators in the sense that it requires neither exact knowledge of system dynamics nor a direct measurement of external disturbance on one hand, and on the other hand, it can counteract the adverse impact of hybrid attacks on the executive capability of the actuator, regardless of the seriously tampered data on the sensor-controller channel. To overcome technical difficulties from hybrid attacks on both channels, we generalize the Markov-parameter-based time-series control method to generate a data packet containing the current and future control inputs, which are further compromised on the controller-actuator channel. Thus, a recovery procedure is additionally designed to solve the model-free output regulation problem by distinguishing the undamaged predicted inputs based on the proposed hybrid attack detection procedure.