Fault-Tolerant Consensus for Leader-following Multi-Agent Systems under False Data Injection Attacks
Dengfeng Pan, Zhihai Wu · 2021
This paper aims to design an adaptive fault-tolerant control protocol for a class of nonlinear leader-following multi-agent systems under false data injection attacks. First, radial basis neural networks are used to approximate the derivative of the unknown nonlinear functions and unknown upper bounds of false data injection attacks. Based on these approximations, an adaptive fault-tolerant control protocol is proposed to guarantee the secure leader-following consensus. Afterward, the feasibility of the proposed protocol is proved by the Lyapunov stability theorem. Finally, simulation results are presented to demonstrate the effectiveness of the designed fault-tolerant control protocol.