Adaptive neural funnel control for a class of pure-feedback nonlinear systems with event-trigger strategy

Abstract

In this paper, an event-trigger-based adaptive funnel control problem is considered for a class of pure-feedback nonlinear systems. Due to the nonaffine variables existed in the virtual controls of pure-feedback systems, the implicit function theorem and mean-value theorem are adopted to guarantee the existence of the ideal virtual controls, which can be effectively approximated by neural networks. Then, a novel event-triggered control strategy is designed to consume less communication resources. The event-triggered condition depends on the amplitudes of the control input, the tracking error and a fixed threshold, which makes the control more flexible in real applications. Furthermore, the proposed control scheme ensures the transient and steady state performance for the tracking errors by constructing a funnel constraint function. Also, the stability analysis proves that all the signals of the closed-loop system are uniformly ultimately bounded. Finally, the feasibility and effectiveness of the proposed control scheme are verified through the simulation.