Dynamical Neural Network Based Dynamic Inverse Control Method for a Flexible Air-Breathing Hypersonic Vehicle

Abstract

A novel dynamic inverse control method based on a dynamical neural network (DNN) is proposed for the trajectory tracking control of a flexible air-breathing hypersonic vehicle (FAHV). Firstly, considering that the accurate model of FAHV is difficult to obtain, the FAHV is regarded as a completely unknown system, and a DNN is designed to identify its nonlinear model. On the basis of Lyapunov’s second law, the weight vectors of the DNN are adaptively updated. Then, a dynamic inverse controller is designed based on the identification model, which avoids the transformation of the nonlinear model of FAHV, thereby simplifying the controller design process. The simulation results verify that the DNN can identify FAHV accurately, and velocity and altitude can track the given reference signal accurately with the proposed dynamic inverse control method. Compared with the back-stepping control method, the proposed method has better tracking accuracy, and the amplitude of the initial control law is smaller.