Network-Based $$H_\infty $$ Filtering for Descriptor Markovian Jump Systems with a Novel Neural Network Event-Triggered Scheme

Abstract

This paper studies network-based $$H_\infty $$ filtering problem for descriptor Markovian jump systems with a novel neural network event-triggered scheme. Firstly, to save more limited communication bandwidth, a novel neural network event-triggered scheme is introduced to dynamically adjust communication bandwidth based on desired filtering performance. Secondly, an event-triggered mode-dependent $$H_\infty $$ filter is designed for descriptor Markovian jump system. By considering the network-induced delay and the event-triggered scheme, a delay system method is used to build a novel filtering error system model. By using Lyapunov function technology and free weighting method, the criteria are obtained in terms of LMIs which guarantee the filtering error system to be regular, impulse free and stochastically stable with the $$H_\infty $$ performance. Then, a co-design method is proposed for the designed filter parameters. Finally, a numerical simulation example is employed to illustrate the effectiveness, and by a compared example, we show that the number of transmitted data produced by the proposed neural network event-triggered scheme is less than those produced by traditional event-triggered scheme.