宇航学报

宇航学报 ›› 2021, Vol. 42 ›› Issue (10): 1228-1236.doi: 10.3873/j.issn.1000-1328.2021.10.004

• 制导、导航、控制与电子 • 上一篇    下一篇

运载火箭推力故障下基于智能决策的在线轨迹重规划方法

谭述君，何骁，张立勇，吴志刚   

  1. 1. 大连理工大学工业装备结构分析国家重点实验室，大连 116024；
    2.大连理工大学辽宁省空天飞行器前沿技术重点实验室，大连 116024; 3. 大连理工大学电子与信息工程学院，大连 116024
  • 收稿日期:2020-12-01 修回日期:2021-02-01 出版日期:2021-10-15 发布日期:2021-10-15
  • 基金资助:
    国家自然科学基金(11972101, 62076050)；国防科技基础加强计划资助

Online Trajectory Replanning Method Based on Intelligent Decision making  for Launch Vehicles under Thrust Drop Failure

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

  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

  • Received:2020-12-01 Revised:2021-02-01 Online:2021-10-15 Published:2021-10-15

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摘要： 为了提高运载火箭上升段飞行中推力故障下轨迹重规划的计算效率，提出了一种基于智能决策的在线轨迹重规划方法，将原问题转化为最优救援轨道的在线智能决策和成熟的燃料最优轨迹规划问题进行求解。通过离线求解不同故障状态对应的轨迹重规划问题，建立“故障状态-救援轨道”样本集，基于径向基神经网络建立最优救援轨道的决策模型，并将决策模型装订箭上。在实际飞行中以当前故障状态作为输入，利用决策模型可在线快速决策出最优救援轨道根数，进而求解以救援轨道为目标轨道的燃料最优轨迹规划问题，从而完成推力故障下的在线轨迹重规划。数值仿真表明，与直接求解轨迹重规划问题相比，该方法的计算效率提高了两个数量级以上，同时给出一致的最优救援轨迹。


关键词: 运载火箭, 最优救援轨道, 在线轨迹优化, 智能决策, 径向基神经网络, 自适应伪谱法

Abstract: In order to improve the computational efficiency of trajectory replanning in ascending flight of launch vehicles experiencing thrust drop fault, an online trajectory replanning method based on the intelligent decision making is proposed by transforming the original optimization problem into two independent problems, i.e. the intelligent decision making of the optimal rescue orbit and the optimization problem to achieve minimum fuel consumption (MFC). The “fault state rescue orbit” sample dataset is generated by offline solving the trajectory replanning problem with all kinds of different thrust drop fault states. Based on the sample dataset, the decision making model of the optimal rescue orbit is established by training a radial basis function neural network. The rescue orbit decision model is online used in actual flight to decide the optimal rescue orbit elements corresponding to the current thrust drop fault state. Subsequently, the MFC problem using the rescue orbit as the target orbit is solved. And so the optimal rescue trajectory of the launch vehicle under thrust drop failure is obtained online. The numerical simulations show that the proposed method improves the computational efficiency by more than two orders of magnitude and gives the same optimal rescue trajectory, compared with solving the original trajectory replanning problem directly.


Key words: Launch vehicle, Optimal rescue orbit, Online trajectory optimization, Intelligent decision making, Radial basis function neural network, Adaptive pseudospectral method

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