Robust consensus for linear multi-agent systems with noises

This study investigates robust consensus problem for multi-agent systems (MASs) with both system and communication noises. The agent state is estimated based on the noisy measurement information by applying Kalman-filtering theory, and then the estimated state is sent to the neighbour agents through a noisy communication environment. A consensus protocol is proposed based on the relative estimated state, under which the consensus error of the MASs can be characterised precisely via the unique solution of a Lyapunov equation. In addition, the consensusability via dynamic output feedback control of the MASs without noises is studied. Finally, simulation examples are given to show the correctness of the proposed results.

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