Event-Triggered Resilient Matrix-Scaled Consensus of Heterogeneous Multi-Agent Systems

This paper investigates the resilient matrix-scaled consensus problem for continuous-time heterogeneous multi-agent systems in the presence of bounded malicious nodes. Each agent is assigned a predefined positive/negative matrix weight. Unlike existing research on consensus problems, this work adopts a generalized matrix-scaled approach to accommodate the independence of multi-dimensional states, extending the final convergence subspace to a broader open region within the state space rather than restricting it to a common value or specific ratio. An event-triggered communication-based matrix-scaled resilient algorithm is proposed to mitigate the influence of malicious nodes on normal agent convergence while significantly reducing communication overhead among agents. Furthermore, by employing system transformation, necessary and sufficient conditions for network topology are established to address the resilient matrix-scaled consensus problem. The effectiveness of theoretical result is further verified by a numerical simulation.