Prescribed-time adaptive neural feedback control for a class of nonlinear systems

This paper studies prescribed-time stabilization for a class of unknown strict-feedback nonlinear systems. Adaptive time-varying feedback control method and non-scaling transformation strategies are employed to reduce the computation burden caused by the scaling functions. Besides, the proposed method uses a constrained function to avoid the possibility of an oversized signal when time approaches to the prescribed settling time. Based on the backstepping method, a neural prescribed-time controller is constructed to achieve finite-time regulation. Under the action of the proposed strategy, all the closed-loop signals are bounded and the system is stable in prescribed finite time. Finally, two simulation examples illustrate the effectiveness of the results.