Remote State Estimation and Instruction Adjustment of Multi-Agent Systems Under False Data Injection Attacks

This paper investigates the problems of remote state estimation and instruction adjustment for multi-agent systems (MASs) under simultaneous sensor and instruction attacks, as well as external disturbances. Unlike existing research that primarily focuses on single-channel attacks, this paper investigates the more challenging scenario of dual-channel attacks. To decouple the detrimental effects of instruction attacks on state estimation, an augmented descriptor system is constructed, treating sensor attacks as part of an extended state. Then, a distributed unknown input observer is designed to estimate the system states. Theoretical analysis demonstrates that state estimation error is asymptotically convergent and independent of instruction estimation. Furthermore, to counteract instruction attacks, a distributed cooperative estimation strategy based on an adaptive saturation mechanism is proposed, enabling all agents to achieve bounded estimation of the true reference instruction within a finite number of iterations. Finally, the effectiveness and robustness of the proposed approach under dual-channel attacks are verified by a simulation of an unmanned vehicle platoon.