圆轨道任务火箭推力故障下的在线任务重构研究  

Research on Online Mission Replanning Method of Rocket Executing Circular Orbit Mission under Thrust Failure

作　　者：马宗占 许志[1,2] 王传魁 唐硕 马英[3] MA Zongzhan;XU Zhi;WANG Chuankui;TANG Shuo;MA Ying(School of Astronautics,Northwestern Polytechnical University,Xi’an 710072;Shaanxi Aerospace Flight Vehicle Design Key Laboratory,Xi’an 710072;Beijing Institute of Astronautical Systems Engineering,Beijing 100076)

机构地区：[1]西北工业大学航天学院,西安710072 [2]陕西省空天飞行器设计技术重点实验室,西安710072 [3]北京宇航系统工程研究所,北京100076

出　　处：《宇航学报》2025年第1期68-81,共14页Journal of Astronautics

摘　　要：为使圆轨道任务火箭在发生推力下降故障以至于无法完成原定任务的情况下尽可能利用剩余燃料进入降级轨道,针对两种新类型降级轨道提出了一种深度神经网络(DNN)与无损凸优化融合的任务重规划算法。首先通过对原非凸轨道规划问题进行等价转换、对约束进行无损松弛和引入附加滑行段等方式,提出了一种将问题降维求解的两层优化算法,将原问题转化成无损凸化的内层问题和单变量优化的外层问题,从而保障收敛。然后引入DNN代替外层算法输出单变量,同时保留内层无损凸优化算法以避免对DNN输出精度的依赖,实现了以最小风险提升计算效率。数值仿真证明该融合算法不仅能保障精度与最优性,还有收敛快以及性能稳定的特点,具有较强的工程应用价值。To maximize the utilization of remaining fuel for a rocket on a circular orbit mission to enter a degraded orbit in the event of a thrust drop failure preventing mission completion,a mission replanning algorithm that integrates deep neural networks(DNN)with lossless convex optimization is proposed for two novel types of degraded orbits.Initially,a two-layer optimization algorithm is formulated to diminish the dimensionality of the original non-convex problem through equivalent transformation,lossless relaxation of constraints,and the incorporation of extra coasting segments.This approach converts the problem into an inner layer that is amenable to lossless convexification and an outer layer that can be optimized using a single variable to guarantee convergence.Subsequently,DNN is employed to substitute for the outer layer algorithm in generating a single variable,while preserving the inner layer's lossless convex optimization algorithm to eliminate reliance on DNN output accuracy,thereby achieving an optimal enhancement in computational efficiency with minimal risk.Ultimately,numerical simulations demonstrate that the hybrid algorithm not only ensures accuracy and optimality but also exhibits rapid convergence and stable performance,making it highly valuable for engineering applications.

关 键 词：运载火箭 圆轨道 任务重构 推力下降故障 无损凸优化 深度神经网络 

分 类 号：V448.133[航空宇航科学与技术—飞行器设计]