Intermittent Sampled-Data Stabilization of Highly Nonlinear Delayed Stochastic Networks via Periodic Self-Triggered Strategy

Abstract

This article considers the stabilization issue of highly nonlinear delayed stochastic networks (HNDSNs) based on periodic self-triggered intermittent control under sampled-data (PICS) for the first time. Therein, the linear growth condition is taken off, and some PICS-based stabilization conditions in previous works are weakened. It is worth pointing out that the existing results are suitable for highly nonlinear networks neither based on PICS nor considering the time-varying delay. Given this, the existence of the unique global solution of HNDSNs under PICS is discussed, and then a stabilization criterion is derived by utilizing a modified Lyapunov function. After that, a numerical example of central pattern generator networks for a hexapod robot is given for demonstration.