Quasi-Bipartite Synchronization of Derivatively Coupled Complex Dynamic Networks: Memory-Based Self-Triggered Approach

This article is devoted to studying the bipartite synchronization issue of multicoupled complex dynamic networks with mismatched parameters. A generalized processing analysis method for dealing with the network with derivative coupling is first presented. To acquire suitable input intervals, a novel memory-based self-triggered impulsive controller is elaborately designed. Accordingly, the triggering moments are precisely determined, which take the average error states of a small fraction of monitoring moments into account. Sufficient conditions for the bipartite synchronization are eventually obtained by utilizing the Lyapunov stability theorem in conjunction with the parameter variation approach and definition of average impulsive interval. Since time-varying impulsive effects are considered, the definition of average impulsive gain is introduced in order to estimate the convergence rates and error bounds, respectively. The capability of derived mathematical deductions is ultimately demonstrated by numerical examples. Further, comparative experiments are given to show the superiority of performance within three event-based mechanisms.