电子电气工程与控制

基于凸优化的严格回归轨道自主轨迹捕获控制

  • 岳杨 ,
  • 王嘉轶 ,
  • 杨盛庆 ,
  • 杜耀珂 ,
  • 王文妍
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  • 1.上海航天控制技术研究所,上海 201109
    2.上海市空间智能控制技术重点实验室,上海 201109
.E-mail: 1546232863@qq.com

收稿日期: 2025-10-22

  修回日期: 2025-11-13

  录用日期: 2025-12-15

  网络出版日期: 2025-12-25

Convex optimization based autonomous trajectory capture for strictly-regressive orbit

  • Yang YUE ,
  • Jiayi WANG ,
  • Shengqing YANG ,
  • Yaoke DU ,
  • Wenyan WANG
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  • 1.Shanghai Aerospace Control Technology Institute,Shanghai 201109,China
    2.Shanghai Key Laboratory of Aerospace Intelligent Control Technology,Shanghai 201109,China
E-mail: 1546232863@qq.com

Received date: 2025-10-22

  Revised date: 2025-11-13

  Accepted date: 2025-12-15

  Online published: 2025-12-25

摘要

面向卫星严格回归轨道初始化的在轨自主性及鲁棒性运行需求,采用小推力自主制导控制方法,旨在实现卫星在给定任务时间及管径约束条件下,燃料最优的空间大范围自主轨迹精确捕获。以非奇异相对轨道要素为状态变量,基于管径偏差与相对轨道要素的映射关系,建立严格回归轨道动力学优化模型,其状态转移矩阵纳入大气阻力摄动、日月三体引力多种摄动效应。通过几何管径约束的二阶锥形式表达,构建凸优化问题,实现在轨优化的实时运行及稳定收敛。对凸优化推力指令正则处理,生成符合推力器实际开关特性的推力序列。通过嵌入模型预测控制的滚动时域优化框架,将闭环控制转化为时序迭代凸优化问题,确保空间轨迹捕获精度。

本文引用格式

岳杨 , 王嘉轶 , 杨盛庆 , 杜耀珂 , 王文妍 . 基于凸优化的严格回归轨道自主轨迹捕获控制[J]. 航空学报, 2026 , 47(8) : 332954 -332954 . DOI: 10.7527/S1000-6893.2025.32954

Abstract

To meet the operational requirements of on-board autonomy and robustness for the initialization of satellite strictly-regressive orbits, a low-thrust autonomous guidance and control method is adopted, aiming to achieve fuel-optimal precise capture of large-scale spatial trajectories under given mission duration and tube diameter constraints. Using non-singular relative orbital elements as state variables, a dynamic optimization model for strictly-regressive orbits is established based on the mapping relationship between tube diameter deviations and relative orbital elements. The state transition matrix incorporates multiple perturbation effects, including atmospheric drag, solar and lunar third-body gravitational perturbations. By expressing the geometric tube diameter constraints in second-order cone form, a convex optimization problem is formulated to enable real-time on-board optimization with stable convergence. The convex optimization-derived thrust commands are regularized to generate thrust sequences that conform to the actual on-off characteristics of thrusters. Through a receding horizon optimization framework embedded with model predictive control, the closed-loop control is transformed into a time-sequential iterative convex optimization problem, thereby ensuring high precision in spatial trajectory capture.

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