An Optimal Estimation Framework of Multi-Agent Systems With Random Transport Protocol

The state estimation problem of heterogeneous multi-agent systems with random transport protocol was investigated in this paper. Due to the dependency of the agent dynamics and the random sparse structure induced by the random transport protocol, the optimal state estimation design becomes complex and challenging. An optimal state estimator is effectively designed by the Hadamard product and gradient method. Based on the analysis of the matrix functions, a sufficient condition is developed to guarantee that the average estimate error covariance is limited. Finally, a numerical example and a smart grid model are utilized to demonstrate the efficacy of the suggested estimator.