Remote Robust State Estimation for Nonlinear Cyber-Physical Systems Under Denial-of-Service Attacks

In this paper, we investigate the remote robust state estimation problems for nonlinear cyber-physical systems under denial-of-service attacks. The well-known extended Kalman filter is a commonly used method for state estimation of nonlinear systems. However, it does not take into account unmodeled dynamics or parametric uncertainties caused by first-order approximations, which makes its estimation performance unsatisfactory. In addition, denial-of-service attacks prevent the sensor from sending measurements to a remote state estimator by congesting the communication channel, which further deteriorates the estimation performance. To surmount these problems, a robust state estimation algorithm is developed based on sensitivity penalization, taking into account an explicit packet loss parameter. Under certain conditions, the boundedness of estimation error is proved. By selecting a negative resistance oscillation circuit for numerical simulations, it is demonstrated that the remote robust state estimator can markedly improve the estimation performance.