Game-based resilient consensus of multi-agent systems

In this paper, we are interested in how to achieve consensus among normal agents for a class of multi-agent systems (MASs) with malicious agents, in which the states of agents are determined by minimizing their cost functions. We first equip each agent with two states: desired state and behavior state, in which the behavior state is used to interact with the neighbors. Then, using the multi-round information filtering technique, we design a security fault-tolerant algorithm, and develop a sufficient topological condition that can ensure the consensus of the desired states among the normal agents. Finally, we provide a numerical example to demonstrate the effectiveness of the theoretical results.