GEO航天器轨道机动控制研究进展

doi:10.12305/j.issn.1001-506X.2026.01.26

摘要/Abstract

摘要：

随着卫星机动能力的不断提升，地球静止轨道（geostationary Earth orbit，GEO）航天器执行空间任务时的安全问题不容忽视。首先，针对目前编队航天器轨道机动中常用的脉冲推力模型和连续推力模型进行综述，并按照机动过程中的航天器数量区分“一对一航天器轨道机动”和“多航天器轨道机动”；其次，分析了微分对策理论、人工智能算法和生物群体智能算法在解决编队航天器轨道机动问题中的异同优劣；最后，从动力学模型、航天器数量类型和求解方法的视角就编队航天器轨道机动问题的特点进行对比分析。未来的研究重点在于提高算法效率及鲁棒性、增强模型适应性，以实现更加精确和高效的太空管理，保障GEO航天器在轨运行的稳定性和安全性。

关键词: 编队航天器, 轨道机动, 纳什均衡, 微分对策, 深度强化学习

Abstract:

With the continuous improvement of satellite maneuverability, the safety of geostationary Earth orbit （GEO） spacecraft during mission operations cannot be ignored. Firstly, the impulsive thrust model and continuous thrust model commonly used in formation spacecraft orbital maneuvers are reviewed, categorizing them into “one-to-one spacecraft orbital maneuvers” and “multi-spacecraft orbital maneuvers” based on the number of spacecraft involved. Secondly, the similarities, differences, advantages of differential game theory, artificial intelligence algorithms, and biological swarm intelligence algorithms in solving orbital maneuver problems of formation spacecraft are analyzed. Finally, a comparative analysis of the characteristics of formation spacecraft orbital maneuver problem is conducted from the perspectives of dynamic models, spacecraft quantity/types, and solution methods. Future research will focus on improving algorithm efficiency and robustness, enhancing model adaptability, and achieving more precise and efficient space management to ensure the stability and safety of GEO spacecraft in-orbit operations.

Key words: spacecraft formation flying, orbital maneuver, Nash equilibrium, differential game, deep reinforcement learning

中图分类号:

V 448.21

薛锦妍, 张雅声, 陶雪峰, 杨茗棋, 赵帅龙. GEO航天器轨道机动控制研究进展[J]. 系统工程与电子技术, 2026, 48(1): 290-300.

Jinyan XUE, Yasheng ZHANG, Xuefeng TAO, Mingqi YANG, Shuailong ZHAO. Advances in orbital maneuver control for GEO spacecraft[J]. Systems Engineering and Electronics, 2026, 48(1): 290-300.

图/表 4

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模型	适用场景	优点	缺点
脉冲推力	操控性较强的场景	燃料利用率高，适合快速变轨	描述和求解过程更加复杂
连续推力	信息不对称及操控性稍弱的场景	可以在较长时间内对轨道和速度进行精细调整	不适用于需要快速响应的任务

控制类型	适用场景	优点	缺点
一对一	简单的空间任务场景	模型简单，易于分析，可以得到最优解	对模型误差的适应性较差，难以实时决策
多航天器	高动态、大范围的机动控制场景	机动性强，具有快速响应能力，灵活度高，系统弹性强	模型复杂，计算量大，对算法设计和优化要求高

方法	说明	优势	缺点
微分对策法	通过对机动控制问题数学建模，用微分方程表达对策规律，最后通过解析法或数值法对方程进行求解，得到双方最优控制率	（1）具有数学理论支撑，有一定可解释性（2）在适用的场景下能够精确地求解出最优策略	（1）对成本函数的设计有一定的约束，过于复杂的成本函数无法利用传统的方法进行求解（2）需要系统的模型才能使用
人工智能方法	以MADDPG、深度Q学习算法（deep Q-learning，DQN）、自适应动态规划（adaptive/approximate dynamic programming，ADP）为代表，对机动控制双方控制策略进行实时求解	（1）网络可学习智能体策略行为，不受模型影响（2）能够利用复杂的成本函数对任务需求进行更为贴切的描述（3）应用场景更广泛	（1）需要一定的数据样本（2）训练耗时较长（3）相较微分对策法而言，可解释性较差
生物群体智能算法	以蚁群、蜂群、狼群为代表，分析双方遵循的规则，侧重于建模个体行为以及局部存在的规则	（1）具有良好的并行和全局搜索能力（2）对于问题的初始条件和参数设置不敏感，鲁棒性较好	（1）参数选择困难（2）收敛速度慢（3）可能陷入局部最优解

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