Secure Consensus Control of Multiagent Systems With Byzantine Attacks and Multihop Robust Communication Graph

This article investigates the secure consensus tracking control problem for nonlinear multiagent systems under Byzantine attacks and unknown nonlinearities. Addressing structural limitations and vulnerabilities in sparse topologies, the multihop mean-subsequence-reduced algorithm is adopted to enhance system robustness and information availability by enabling message relay through healthy intermediate nodes. Differing from existing works primarily on linear dynamics and static average consensus, this study focuses on the multihop mean-subsequence-reduced method for nonlinear state-feedback control frameworks. Radial basis function (RBF) neural networks are integrated for secure approximation of unknown nonlinearities. Using the backstepping method, a novel secure control strategy is synthesized. The proposed scheme rigorously guarantees the convergence and boundedness of the closed-loop system, ensuring accurate tracking for nonlinear multiagent systems.