Periodic self-triggered intermittent sampled-data stabilization for stochastic complex networks

In this paper, a new class of periodic self-triggered intermittent control with sampled-data (PSICS) is designed to tackle the stabilization issue for stochastic complex networks with time delays and Lévy noise (SCNTL). Therein, the self-triggered scheme is propounded with regard to intermittent control which is periodic judgment, and there exists a sampled-data control in every intervals of periodic judgment in the control time (work time) of intermittent control. It is worth pointing out that PSICS possesses more flexibility in terms of applications and simpler design of triggered conditions, compared with some previously reported control strategies such as the control combines the advantages of the periodic sampling and self-triggered control. Meanwhile, by means of stability analysis, sampled-data control, intermittent control and event-driven control theory, a useful criterion is established to guarantee the exponential stability in mean square of SCNTL. Notably, the stabilization issue of single-link robot arms with time delays and Lévy noise via PSICS is studied, as a practical application of SCNTL. Ultimately, numerical simulations are utilized for illustration.