Adaptive neural preassigned-time tracking control for stochastic nonlinear systems with output constraint and its application

Abstract

This paper considers an adaptive neural preassigned-time tracking control scheme for non-strict feedback stochastic nonlinear systems subjected to output constraint. Depending on the prescribed performance control idea, the output constraint is resolved. Meanwhile, combined with backstepping control scheme, the novel preassigned-time tracking controller is developed, which can not only make the system output satisfy the constraint condition, but also the track error is constrained within a preassigned region in a finite time, where the settling time is arbitrarily set and independent of any parameters. Furthermore, all signals are semi-globally uniformly ultimately bounded in the closed-loop system. Finally, the validity of the proposed control scheme is demonstrated via the practical one-link manipulator.