Control Surface Optimization of Hypersonic Vehicle Based on Adaptive Backstepping Method

Hypersonic vehicle uses air breathing scramjet in its cruise segment. The strong inherent coupling among the aerodynamics, propulsion, structure, and control dynamics is existing, and the high nonlinear dynamic model and serious uncertainty are inevitable. Meanwhile in the cruise segment, hypersonic vehicle should be seized of good stability, command tracking ability and the capability to suppress disturbances. This paper presents a control surface optimization method, which takes the control performance as the optimization target. In this paper, a concurrent subspace optimization (CSSO) based on multi-objective genetic algorithm (MOGA) is proposed to optimize the aerodynamic model and control system of the vehicle. Then a response surface model (RSM) for aerodynamic subsystem is approximated by training a back propagation neural network. Due to the nonlinearity, and aerodynamic parameter uncertainty of the hypersonic vehicle longitudinal kinematics and dynamics model, an altitude control system based on adaptive backstepping method is designed. Under the gust disturbance, the simulation of the hypersonic vehicle with optimal control surface in the cruise segment is carried out. The simulation results indicate that the adaptive backstepping control system is able to overcome the uncertainty and external disturbances, as well as the optimal control surface can enhance the ability of the hypersonic vehicle to track command accurately and suppress disturbance rapidly.