Intelligent Trajectory Generation Method for Hypersonic Glide Vehicles Based on RBF Neural Networks

In this paper, a radial basis function (RBF) neural network based trajectory generation strategy is proposed to solve the online rapid generation of initial reference trajectory for low-cost hypersonic glide vehicles (HGV) under initial state perturbation. Firstly, the feasible trajectories that constitute the sample sets are offline generated by pseudospectral method according to the possible distribution of heights and velocities. Then, the sample set is randomly divided into training subset and test subset, by which the RBF neural network is trained and verified. Moreover, the input of the RBF neural network is a vector comprised by height and velocity from the initial state, whereas the output is a discrete state-control sequence which represents the trajectory from the current state to the expected final state. The simulation results validate that the proposed method has high confidence and small errors, which can improve the on-line generation efficiency of the trajectory.