Fixed-Time Adaptive Neural Prescribed Performance Control for Nonlinear MIMO Singularly Perturbed Systems

This paper focuses on the adaptive neural fixed-time prescribed performance control for a class of multi-input and multi-output (MIMO) nonlinear singularly perturbed systems (SPSs). At first, a fixed-time prescribed performance function is introduced to adjust the transient and steady-state performance of the system, which enables the tracking errors can converge to a preassigned area within a given fixed time. Then, by integrating the fixed-time stability and the backstepping control technique, an adaptive neural fixed-time trajectory tracking controller is designed to guarantee that all the signals in the closed-loop system (CLS) are semi-global uniformly ultimately practically fixed-time bounded. Finally, the effectiveness of the developed control approach is verified through simulation results.