Partially Measured State Estimation of Complex Dynamical Networks with Random Data Loss

Abstract

The state estimation problem is a very important and interesting issue in the field of complex dynamical networks. Yet, the constructions of the state estimators in most existing literature studies all need to measure the output information of all nodes. However, the practical situation is that not all nodes could be measured to obtain the output information. Thus, in this paper, the state estimation is realized while only measuring the output data of partial nodes for a complex dynamical network with random data loss existing on its external communication links to the observers, where the lost data are compensated by the corresponding observer data. Depending on whether the network possesses the root strongly connected components that have a perfect matching, the choice problem of measured nodes and the construction problem of specific output matrices are discussed. By applying the Lyapunov stability theory and stochastic analysis method, a sufficient condition for state estimation is given. Through simulation experiments, the effectiveness of the proposed state estimation scheme is demonstrated.