Event-based state estimation for a class of nonlinear discrete-time complex networks with stochastic noises
In this paper, the state estimation problem is investigated for a class of discrete-time complex networks under the event-triggered framework. The event-based estimator receives the updated measurements from the sensors only when the prespecified event-triggering rule is violated. Compared with the traditional estimator with the clock driven rule, a series of event-based state estimators are developed so as to reduce unnecessary data transmissions in the communication channel. Attention is focused on the analysis and design problem of the event-based estimators for the addressed discrete-time complex networks such that the estimation error is exponentially bounded in mean square. Some sufficient conditions are obtained to ensure the existence of the desired estimators and the upper bound of the estimation error is derived. By using the convex optimization technique, the gain parameters of the desired estimators are obtained in an explicit form. Finally, a numerical example is used to show the effectiveness of the proposed estimation approach.