State Estimation for Stochastic Singularly Perturbed Complex Networks Under New Dynamic Event-Triggered Mechanism

This paper studies the estimation issue for stochastic singularly perturbed complex networks (SPCNs) under a dynamic event-triggered mechanism (ETM). The SPCN is with a Markov chain whose transition probabilities are dependent on a stochastic variable that takes values with known sojourn probabilities. A new ETM is proposed to reduce the use of network resources. We design a state estimator which ensures the estimation error dynamics to be stochastically stable with $H_{\infty}$ performance. By matrix inequality technology, the desired parameters of state estimator are obtained. The effectiveness of the event-triggered estimation method is shown via a numerical example.