A Sampled-Data Based Average Consensus Protocol for Double-Integrator Multi-Agent Systems with Switching Topologies and Communication Noises

A distributed sampled-data based protocol is proposed for the average consensus of double-integrator multiagent systems with switching topologies and communication noises. The proposed protocol uses information from two aspects: the agent's own state and the relative states between the agent and its neighbors. Due to the existence of communication noises, the relative states cannot be obtained accurately. To deal with this difficulty, a time-varying consensus gain is employed to attenuate the noise effect. Under the proposed protocol, it is proved that sufficient conditions for ensuring mean square average consensus are: the consensus gain satisfies the stochastic approximation type condition and the communication topology graph at each sampling instant is a balanced graph with a spanning tree. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed consensus protocol.