Event‐triggered annular finite‐time H∞$$ {H}_{\infty } $$ filtering for discrete‐time mean‐field stochastic systems

Abstract

This article presents a framework for annular finite‐time filtering problem for discrete‐time mean‐field stochastic systems with state‐ and disturbance‐dependent noise. Both static and dynamic event‐triggered mechanisms are introduced, which are related to two different thresholds and internal variables, respectively. Based on mean‐field theory and the Lyapunov functional method, two groups of sufficient conditions are derived to ensure the filtering error systems have annular finite‐time boundedness under an performance level. Then, the desired filter gains can be obtained by solving two convex optimization problems. Finally, two numerical examples are given to show the validity of the proposed results.