Mean-Subsequence-Reduced Fuzzy Adaptive Secure Consensus Control for Multi-Agent Systems With Byzantine Attacks

This paper investigates the secure consensus control problem for strict-feedback nonlinear multi-agent systems with Byzantine attacks and unmeasurable states. Firstly, a fuzzy state observer is constructed to reconstruct unmeasurable states. Then, the mean-subsequence-reduced algorithm is used to design the secure control scheme, which employs a data filtering strategy rather than an agent identification strategy. Specifically, the healthy agent sorts the state information received from its neighbors and discards the extreme values, and the Byzantine attack issue is solved. The proposed control scheme can guarantee that all the signals in the closed-loop system are bounded and the consensus tracking errors converge to a small neighborhood of the origin. Finally, simulation results are given to verify the effectiveness.