Adaptive Resilient Tracking Control With Dual-Terminal Dynamic-Triggering for a Linear Multi-Agent System Against False Data Injection Attacks

Abstract

False data injection (FDI) attacks on sensors and actuators are adverse in multi-agent systems (MASs), and they degrade system performance and even cause instability by tampering transmission signals. Resilient control for such attacks is critical in the real world. In this paper, a distributed adaptive resilient control scheme is proposed. This scheme is based on observers for reconstructing signals with projection operators and dual-terminal dynamic event-triggered mechanism. The update of the controller and intermittent communication among agents are determined by the proposed dual-terminal triggered mechanism, and two triggered functions work independently in individual follower. Compared with traditional static one, it not only achieves satisfactory consensus performance but also reduces triggered number. Also, state estimators are developed for reconstruction of combined measurements, and continuous monitoring is removed. Theoretical analysis shows that our proposed adaptive resilient scheme attenuates adverse effects of FDI attacks and the followers track the leader with Zeno-free behaviors. Finally, two illustrative examples are presented to verify the effectiveness of our adaptive resilient scheme.