Prescribed‐Time Composite Learning Control for Uncertain Strict‐Feedback Nonlinear Systems

This article proposes a prescribed‐time (PT) neural network composite learning tracking control scheme for a class of strict‐feedback nonlinear systems with functional uncertainties. Specifically, a PT composite learning framework is constructed to improve the estimation performance of the unknown nonlinear functions under the weaker interval excitation (IE) condition. To guarantee PT tracking performance, a PT controller with time‐varying bounded gains is designed to ensure that the tracking error converges to an arbitrarily small neighborhood of zero within the prescribed time, independent of initial conditions. Furthermore, a PT dynamic surface filter is developed to overcome the explosion of complexity inherent in traditional backstepping designs. Finally, numerical simulations and real‐time experiments on a permanent magnet synchronous motor (PMSM) platform are conducted to validate the effectiveness and practical applicability of the proposed control scheme.