Event‐based finite‐time H∞ filtering of discrete‐time singular jump network systems
Yingqi Zhang, Peng Shi, Michael Basin
Abstract
Abstract This article presents the analysis and design problems of event‐based (EB) finite‐time (FT) filtering for discrete‐time singular Markov jump network systems (SMNSs) based on separation of matrix inequality variables. According to the EB method, an SMNS model of network‐induced delay is introduced. Sufficient conditions of singular stochastic FT boundedness are then obtained for the augmented SMNS model by applying a stochastic Lyapunov functional and introducing slack matrix variables. In addition, using separation schemes for decoupling matrix inequality variables, the EBFT filter gain matrices and triggered ones are co‐designed to ensure that the augmented SMNSs are singularly stochastic FT bounded with a prescribed performance index. Finally, a practical DC test example is presented to show effectiveness of the proposed approach.