Wang Min, Liu Huabo
School of Automation, Qingdao University, Qingdao 266071, China.
Shandong Key Laboratory of Industrial Control Technology, Qingdao 266071, China.
Sensors (Basel). 2023 Jul 20;23(14):6553. doi: 10.3390/s23146553.
In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation.
在本文中，我们关注具有恒定测量延迟的非线性网络系统在遭受拒绝服务（DoS）攻击时的事件触发鲁棒状态估计问题。扩展卡尔曼滤波器（EKF）的计算会产生线性化近似误差，这可能导致状态估计误差增加，进而放大线性化误差。DoS攻击通过使通信网络过载来干扰发送到远程鲁棒状态估计器的测量值传输，同时通信信道的通信速率受到限制。因此，推导了一种基于灵敏度惩罚且具有显式数据包到达参数的事件触发鲁棒状态估计算法，以抵御DoS攻击和线性化误差。同时，测量延迟的存在使得无法直接使用传统的状态估计算法，这促使我们设计一种创新的状态扩充方法。数值模拟结果表明，所提出的鲁棒状态估计器能够显著提高状态估计的准确性。
