Event-triggered finite-time adaptive neural control for nonlinear non-strict-feedback time-delay systems with disturbances

Abstract

This paper focuses on the tracking problem for a class of nonlinear time-delay systems with external disturbances in a non-strict-feedback structure, and an event-trigger-based finite-time adaptive neural controller is designed. Suitable Lyapunov-Krasovskii functionals are constructed to deal with the time-delay terms in this system. Combining the structural property of radial basis function neural networks and backstepping methodology, the design difficulty from the non-strict-feedback structure of the system is solved. A finite-time prescribed performance function is introduced to drive the tracking error to a small neighborhood of the origin in a finite time. The proposed event-triggered control scheme has a larger threshold than that of the fixed-threshold scheme to reduce the communication burden and ensure that all of the signals of the closed-loop system are semi-globally uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the proposed scheme.