Event-triggered State Estimation for Dynamics Networks with Stochastic Coupling under Uncertain Occurrence Probabilities
Hongxu Zhang, Jun Feng Hu, Lei Zou · 2018
In this paper, we address the event-triggered state estimation problem for a class of time-varying complex networks subject to multiplicative noises and stochastic coupling under uncertain occurrence probability. The stochastic coupling is modeled by introducing a set of Bernoulli distributed random variables, where the uncertainties of the occurrence probability is characterized. Moreover, the event-triggered mechanism is employed with hope to reduce the network burden and save energy consumption. The aim of the paper is to design the robust state estimator for addressed dynamics networks and derive an optimized upper bound of the estimation error covariance by properly choosing the estimator gain. Finally, simulations and comparisons are provided to verify the validity of the proposed robust state estimation method.