首页|Online rescue method based on offline learning of dynamics knowledge for launch vehicles under thrust-drop fault
Online rescue method based on offline learning of dynamics knowledge for launch vehicles under thrust-drop fault
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
This paper presents an online rescue method based on offline learning of dynamics knowledge to solve the problem of the optimal rescue orbit and flight trajectory optimization (OROTO) of launch vehicles experiencing thrust-drop faults. Due to the unknown of the rescue orbit, solving the OROTO problem by the conventional aerospace orbit and trajectory optimization method is time-consuming. In this paper, benefiting from the decision-making of the optimal rescue orbit by the machine learning technology, the OROTO problem is decoupled into a decision-making of the optimal rescue orbit and a trajectory optimization problem with a known orbit. In the decision-making of the optimal rescue orbit, instead of the conventional iteration optimization process based on dynamics, the optimal rescue orbit is determined by the "fault-rescue" knowledge integration (FRKI) model which consists of probabilistic neural network (PNN) and radial basis function neural network (RBFNN) trained by "fault-rescue" knowledge. In the trajectory optimization part, the output of the FRKI model provides terminal constraints for the trajectory optimization problem to decrease the search scope for the optimal solution. Numerical simulation results show that the proposed method can solve the OROTO problem rapidly and accurately, and can potentially be implemented for online applications. (C) 2021 Elsevier B.V. All rights reserved.
Launch vehicleThrust drop faultRescue orbit and trajectoryMachine learningOffline knowledge integrationOPTIMIZATIONALGORITHMGUIDANCETRAJECTORIESDESIGNORBIT
NSTL
Elsevier
