Adaptive Neural Control for Hypersonic Vehicle Based on Barrier Lyapunov Function

Abstract

In this paper, an adaptive neural backstepping control method based on barrier Lyapunov function is proposed for hypersonic vehicle considering full state constraints. The longitudinal dynamic of hypersonic vehicle can be divided into two subsystems, i.e., altitude subsystem and velocity subsystem and the controllers are designed with backstepping method, respectively. In the designing process, the radial basis function neural networks are used to approximate the unknown nonlinear functions of longitudinal dynamic, therefore, the accuracy requirement of hypersonic vehicle model is largely reduced. In order to handle the explosion of complexity issues occurring in the backstepping method, a tracking differentiator is introduced to calculate the differential of virtual control law. The barrier Lyapunov function is constructed to overcome the full system dynamic state constraints and an auxiliary system is designed for overcome the input state saturation issue. The stability is carried out based on Lyapunov theory, and the signals of closed-loop system established are uniformly ultimately bounded. The simulation results show that the controller designed for hypersonic vehicle can guarantee the good tracking performance.

Keywords

hypersonic vehicle / barrier Lyapunov function / radial basis function neural network / tracking differentiator