Abstract This paper addresses the issue of fixed-time neural adaptive event-triggered control for nonstrict-feedback nonlinear systems with full-state constraints, input dead-zone, and saturation. Radial basis function neural networks (RBFNNs) are used to identify the unknown nonlinearities. The paper considers both input saturation and dead-zone effects, approximating these non-smooth nonlinearities with a non-affine smooth function and then transforming them into an affine form using the mean value theorem. The approach integrates backstepping recursive design with a varying threshold event-triggered condition to create an event-triggered neural adaptive fixed-time control algorithm that employs barrier Lyapunov functions (BLFs) and RBFNNs. By applying the fixed-time stability criterion, the proposed controller ensures that the tracking error converges to a smaller region within a fixed time and that all variables in the closed-loop system remain bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed method.
Kharrat et al. (Thu,) studied this question.