Journal of Astronautics ›› 2022, Vol. 43 ›› Issue (3): 344-355.doi: 10.3873/j.issn.1000-1328.2022.03.010

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Mission Reconstruction Method with Lightweight Online Computation for Launch Vehicles under Thrust Drop Fault

HE Xiao, TAN Shu jun, WU Zhi gang, ZHANG Li yong, LIU Yu xi   

  1. 1. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China;
    2.Key Laboratory of Advanced Technology for Aerospace Vehicles of Liaoning Province, Dalian University of Technology, Dalian 116024, China;
    3.Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;
    4. Shanghai Aerospace System Engineering Institute, Shanghai 201109, China
  • Received:2021-09-30 Revised:2021-12-31 Online:2022-03-15 Published:2022-03-15

Abstract: To avoid the launch failure of launch vehicles caused by thrust drop faults, an online mission reconstruction method based on radial basis function neural network (RBFNN) is proposed, which can quickly obtain the approximate solution of flight trajectory from the fault position to the optimal rescue orbit (optimal trajectory, OT) online. In the offline part, mission reconstruction problems under numerous fault states of the thrust drop are solved by the convex optimization and the adaptive collocation method to generate the dataset about the fault states versus the OT. The dataset is used to train the RBFNN to establish a trajectory decision making model for mapping the relationship from the fault states to the OT. During the online application, instead of iteratively solving the trajectory optimization problem, the RBFNN trained offline is used for forward propagation, the approximate solution of the OT can quickly be obtained by the trajectory decision making model. The effectiveness of the proposed method in the case of circular orbit and elliptical orbit is validated by the numerical simulation. The results show that the online solving times of the proposed method are decreased by more than three orders of magnitude, compared with the direct method.

Key words: Launch vehicle, Thrust drop, Online mission reconstruction, Lightweight computation, Radial basis function neural network

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