Study on Trajectory Optimization of Hypersonic Vehicle Based on Neural Network

Abstract

For the horizontal take-off hypersonic cruise aircraft, research on the combined design method of multi-section was carried out, the main design parameters of different sections were analyzed, the parametric design model of the flight path was established, and the characteristics of the typical flight path were studied. On this basis, the calculation of sample points was carried out, and a prediction model of aircraft range and flight time based on the design parameters of the four main flight sections was established based on the neural network method. The genetic algorithm is used to optimize the flight path of the prediction model with the range as the objective function. The research results show that the neural network prediction model based on the parametric design of the trajectory can predict random sample points better than the trajectory model For the prediction of random sample points, compared with the calculation results of the trajectory model, the maximum errors of the flight range and flight time are within 0.82% and 0.45%. The prediction model is optimized with the flight range as the objective function, and the relative error between the optimal range and the trajectory model under the corresponding section parameters is less than 0.2%, which shows that the model established in this paper can better predict the range and flight time according to the section design parameters. Parametric modeling and neural network optimization are feasible methods for aircraft trajectory design and section parameter optimization.