Adaptive neural network dynamic surface control of hypersonic vehicle with variable geometry inlet

Abstract

In this paper, a longitudinal model of hypersonic vehicle with variable geometry inlet is established. Because of the strict requirements for the angle of attack of the scramjet during the flight of the aircraft, the uncertainty introduced by the parameter fitting, the rotating lip cover in the longitudinal model, and the uncertain external interference of the aircraft. The dynamic surface control technology is used to design the angle of attack autopilot of the aircraft and the Radial basis function (RBF) neural network is used to realize the adaptive approximation of the uncertain part of the model, to suppress the interference and accurately track the instructions. Finally, the simulation results show that the method can effectively control the angle of attack of hypersonic vehicle with variable geometry inlet, meet the performance requirements and verify the correctness of the method.