Neural-Network-Based Adaptive Prescribed Finite Time Control for a Class of Second-Order Nonlinear Singularly Perturbed Systems

This article studies prescribed finite time control (PFTC) subject for a class of second-order nonlinear singularly perturbed systems (SPSs). An adaptive prescribed finite time stability strategy based on neural network (NN) is designed for SPSs. The key to successfully designing this strategy lies in introducing a crucial coordinate transformation during design procedures of the controller. This strategy not only ensures stability of the system but also guarantees that the convergence time of the system neither depends on the initial conditions nor on the design parameters. Meanwhile, this designed strategy is not based on a finite time interval, but rather on an infinite time interval. In the end, a simulation example is given to confirm benefits of designed strategy.