Generation of Entry Vehicle Reachable Domain Based on Neural Network
In order to meet the real-time requirements of the entry domain generation of hypersonic vehicles,a neural network model solution scheme in a pole-changing coordinate system is proposed in this paper.The bank angle profile and attack angle profile were parameterized,and the rapid prediction neural network model of the landing point was constructed in the pole-changing coordinate system to improve the adaptability of the neural network model.The Bayesian regularization method was used to obtain the network weight.Based on the rapid prediction model of ballistic landing points,the reentry vehicle reachable domain was obtained by using linear boundary and elliptic boundary ap-proximation methods.Simulation results show that the proposed method has strong real-time performance and adapta-bility and enables online task planning.
NETL
NSTL
万方数据
