Resilient cluster consensus control for heterogeneous multi-agent systems

This paper investigates the resilient cluster consensus problem for discrete-time heterogeneous multi-agent systems (MASs). A two-round filtering algorithm is designed to enable each normal agent in the network to withstand attacks from malicious agents and achieve cluster consensus. To achieve cluster consensus, the proposed algorithm only requires the network to satisfy robustness conditions and an upper bound on the number of malicious agents, without necessitating complex balance conditions between clusters. Furthermore, the sampling interval and control gains for cluster consensus are derived. Finally, the correctness of our theoretical results is validated through examples.