Sampled-data based resilient consensus of heterogeneous multiagent systems

This article studies consensus of a group of heterogeneous agents with first-order and second-order integrator dynamics in presence of malicious agents. We employ the algorithm where each normal agent ignores large and small relative state values of its neighbors to mitigate the effects of malicious agents. Assuming that the maximum number of malicious agents in the neighborhood of each agent is known, sufficient topological condition is obtained to guarantee resilient consensus in directed networks. The result is further extended to heterogeneous multiagent systems with bounded communication delays. Moreover, impulsive control strategy is introduced in the update schemes and sufficient condition in terms of graph robustness is provided for resilient consensus. Numerical examples are provided to illustrate the effectiveness of the theoretical results.