An Adaptive Generalized Super-Twisting Algorithm via Event-Triggered Control

This paper presents a novel event-triggered adaptive generalized super-twisting algorithm (ET-AGSTA) for uncertain nonlinear systems. The remarkable features of the developed method lie in that the control gains can be dynamically adjusted to avoid overestimation, and the transmission of unnecessary signals can be reduced to save computational costs and network resources. Under the proposed ET-AGSTA, it can be proved by the Lyapunov theory that the sliding variable finite-time converges to a desired domain around the origin. The size of the domain can be achieved by modifying the control parameters. Moreover, the lower bound of the triggering time intervals is verified to be always a positive constant, which guarantees Zeno-free behavior in the whole system. Finally, simulation results are given to demonstrate the effectiveness of the proposed scheme.