Finite time adaptive neural tracking control for non-strict-feedback uncertain non-linear systems with disturbance and input delay

Abstract

In this paper, based on neural networks (NNs), we consider finite time adaptive tracking control scheme for non-strict-feedback uncertain non-linear systems with time-variant external disturbance and input delay. A novel auxiliary system has been introduced to degrade the design difficulty caused by input delay. For the unknown non-linear functions and uncertainties in each step, radial basis function NNs are introduced to approximate them such that the control objective can be obtained. Furthermore, based on the idea of backstepping technique, an effective finite time adaptive neural tracking controller has been obtained in the presence of finite time Lyapunov theory, and the singularity problem that may occur in the design process has been overcome using the piecewise functions method. Using the Lyapunov stability theorem, which shows that all the signals of the closed-loop systems are finite time bounded, and the tracking error fluctuates around the origin. Consequently, the proposed scheme not only solves the tracking problem of non-strict-feedback systems with input delay but also realizes the finite time stability performance constraint. Finally, the simulations of two examples show the superiority of the proposed scheme.