Distributed consensus algorithms have been widely designed for linear time-invariant multiagent systems. However, multiagent systems are subject to unknown environments, which results in time-varying linear models. Additionally, it is desirable to investigate the consensus performance of the closed-loop MAS. In this article, we characterize the operating condition of the multiagent system by an unknown bounded independent parameter. We propose a state space model of the multiagent system where system matrices are polynomial functions of the independent parameter. Based on two different scenarios, we design two LQR-based optimal consensus algorithms that are robust with respect to the MASs varying operating condition. In each case, we calculate the final agreement value in the presence of modeling uncertainties and provide a sufficient condition in order to reach the state agreement on zero.
['Vahid Rezaei', 'Margareta Stefanovic']
Distributed optimal leaderless consensus of linear multiagent systems with polynomial state space models