Intermediate-Variable-Based Robust State Estimation for Cyber-Physical Systems Against FDI Attacks

This brief investigates the robust state estimation problem for cyber-physical systems (CPSs) against false data injection (FDI) attacks. The FDI attack scenario is one in which both the actuator and the sensor are subjected to FDI. The traditional intermediate variable or proportional-integral observer methods can only achieve desired state estimation under slow-varying or constant attack signals. Given the unknown variability of the attack signal, a novel intermediate-variable observer method is designed to estimate the system state synchronously and time-varying FDI attacks, achieving robust state estimation in the presence of time-varying attacks. In addition, the proposed observer's robustness is ensured by establishing the root-mean-square gain that makes the zero initial condition unnecessary, which is easier to adapt to the implementation in engineering. Finally, the simulation is provided to verify the effectiveness of the proposed scheme.