基于改进粒子群算法的月地转移轨道优化

李远平, 蔡远利, 李济生

李远平, 蔡远利, 李济生. 基于改进粒子群算法的月地转移轨道优化[J]. 工程力学, 2020, 37(3): 238-244. DOI: 10.6052/j.issn.1000-4750.2019.04.0176
引用本文: 李远平, 蔡远利, 李济生. 基于改进粒子群算法的月地转移轨道优化[J]. 工程力学, 2020, 37(3): 238-244. DOI: 10.6052/j.issn.1000-4750.2019.04.0176
LI Yuan-ping, CAI Yuan-li, LI Ji-sheng. MOON-TO-EARTH TRANSFER TRAJECTORY OPTIMIZATION BASE ON MODIFIED PARTICLE SWARM OPTIMIZATION[J]. Engineering Mechanics, 2020, 37(3): 238-244. DOI: 10.6052/j.issn.1000-4750.2019.04.0176
Citation: LI Yuan-ping, CAI Yuan-li, LI Ji-sheng. MOON-TO-EARTH TRANSFER TRAJECTORY OPTIMIZATION BASE ON MODIFIED PARTICLE SWARM OPTIMIZATION[J]. Engineering Mechanics, 2020, 37(3): 238-244. DOI: 10.6052/j.issn.1000-4750.2019.04.0176

基于改进粒子群算法的月地转移轨道优化

基金项目: 宇航动力学国家重点实验室基金项目(8011001)
详细信息
    作者简介:

    李远平(1972-),男,广东人,研究员,博士生,主要从事飞行器制导与控制研究(E-mail:liyuanping@stu.xjtu.edu.cn);李济生(1943-2019),男,山东人,教授,博导,院士,主要从事航天器轨道动力学研究(E-mail:lijisheng@cashq.ac.cn).

    通讯作者:

    蔡远利(1963-),男,贵州人,教授,博士,博导,主要从事飞行器制导与控制、系统仿真研究(E-mail:ylicai@mail.xjtu.edu.cn).

  • 中图分类号: O224;V412.4

MOON-TO-EARTH TRANSFER TRAJECTORY OPTIMIZATION BASE ON MODIFIED PARTICLE SWARM OPTIMIZATION

  • 摘要: 月地转移轨道优化是月球返回任务的技术难题之一,其搜索空间大、约束条件多。该文通过罚函数法将多约束优化问题转化为无约束优化问题,提出了一种改进粒子群算法,利用适应度函数来更新惯性权重,对粒子的速度加以约束,还对粒子的位置参数引入随机反馈控制,分析了算法的收敛性。在月地返回窗口内获得了逃逸速度增量最小的月地转移轨道优化结果,并利用目标函数的等高线图分析,对优化结果进行了验证。
    Abstract: Due to large search space and quite a few constraints, the optimization of moon-to-earth transfer trajectory is a challenging problem in lunar return mission. The optimization with multiple constraints is converted to an unconstrained optimization problem with the penalty function method, and a modified particle swarm optimization algorithm is proposed to obtain the optimal moon-to-earth transfer trajectory for the minimum escape velocity increment. A fitness function is used to update the inertia weight, and particle velocity is constrained. Additionally, a random feedback control is applied to the position parameter of particles in a modified algorithm. Furthermore, a convergence performance of the algorithm is analyzed. The optimized result in the lunar return window is verified through the contour map analysis of an objective function.
  • [1] 郑伟, 许厚泽, 钟敏, 等. 月球探测计划研究进展[J]. 地球物理学进展, 2012, 27(6):2296-2307. Zhen Wei, Xu Houze, Zhong Min, et al. Progress in international lunar exploration programs[J]. Progress in Geophysics, 2012, 27(6):2296-2307. (in Chinese)
    [2] Bruno A D. The restricted 3-body problem[M]. New York:Walter de Gruyter, 1994.
    [3] Jesick M, Ocampo C. Automated generation of symmetric lunar free-return trajectories[J]. Journal of Guidance, Control, and Dynamics, 2011, 34(1):98-106.
    [4] Ocampo C, Saudemont R R. Initial trajectory model for multi-maneuver Moon-to-Earth abort sequence[J]. Journal of Guidance, Control, and Dynamics, 2010, 33(4):1184-1194.
    [5] 彭坤, 孙国江, 王平, 等. 地月空间对称自由返回轨道设计与分析[J]. 航天器工程, 2018, 27(6):27-33. Peng Kun, Sun Guojiang, Wang Ping, et al. Design and analysis of symmetrical free-return trajectories in earth-moon space[J]. Spacecraft Engineering, 2018, 27(6):27-33. (in Chinese)
    [6] 王丹阳, 邓辉. 地月自由返回轨道设计[J]. 中国空间科学技术, 2017, 37(1):57-65. Wang Danyang, Deng Hui. Cislunar free return trajectory design[J]. Chinese Space Science and Technology, 2017, 37(1):57-65. (in Chinese)
    [7] 刘玥, 钱霙婧, 马林, 等. 月球低能返回轨道设计的混合自适应遗传算法[J]. 哈尔滨工业大学学报, 2016, 48(4):79-83. Liu Yue, Qian Yingjing, Ma Lin, et al. An adaptive genetic algorithm for low energy lunar return trajectory design[J]. Journal of Harbin Institute of Technology, 2016, 48(4):79-83. (in Chinese)
    [8] Anhorn W. Design of fast earth-return trajectories from a lunar base[D]. Monteray:Naval Postgraduate School, 1991.
    [9] 高玉东, 郗晓宁, 白玉铸, 等. 月球探测器返回轨道快速搜索设计[J]. 宇航学报, 2008, 29(3):765-771. Gao Yudong, Xi Xiaoning, Bai Yuzhu, et al. Fast searching design method for return transfer trajectory of lunar probe[J]. Journal of Astronautics, 2008, 29(3):765-771. (in Chinese)
    [10] Whitley R J, Ocampo C A, Williams J. Performance of an autonomous multi-maneuver algorithm for lunar trans-Earth injection[J]. Journal of Spacecraft & Rockets, 2015, 49(1):165-174.
    [11] 颜欣桐, 徐龙河. 基于遗传算法的钢筋混凝土框架-剪力墙结构失效模式多目标优化[J]. 工程力学, 2018, 35(4):69-77. Yan Xintong, Xu Longhe. Multi-objective optimization of genetic algorithm-based failure mode for reinforced concrete frame-shear wall structures[J]. Engineering Mechanics, 2018, 35(4):69-77. (in Chinese)
    [12] Schnetzler B. Optimization by simulated annealing[J]. Science, 1992, 220(4598):671-680.
    [13] Kennedy J, Eberhart R C. Particle swarm optimization[C]//IEEE International Conference on Neural Networks, Piscataway:IEEE Press, 1995, 4:1942-1948.
    [14] Dorigo M, Maniezzo V, Colorni A. The ant system:Optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1996, 26(1):29-41.
    [15] Karaboga D, Akay B. A comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009, 214(1):108-132.
    [16] 杨振伟, 周广东, 伊廷华, 等. 基于分级免疫萤火虫算法的桥梁振动传感器优化布置研究[J]. 工程力学, 2019, 36(3):63-70. Yang Zhenwei, Zhou Guangdong, Yi Tinghua, et al. Optimal vibration sensor placement for bridges using gradation-immune firefly algorithm[J]. Engineering Mechanics, 2019, 36(3):63-70. (in Chinese)
    [17] 汪超, 谢能刚, 黄璐璐. 基于扩展等几何分析和混沌离子运动算法的带孔结构形状优化设计[J]. 工程力学, 2019, 36(4):248-256. Wang Chao, Xie Nenggang, Huang Lulu. Design and shape optimization of holed structure by extended isogeometric analysis and chaotic ion motion optimization[J]. Engineering Mechanics, 2019, 36(4):248-256. (in Chinese)
    [18] Wang D S, Tan D P, Liu L. Particle swarm optimization algorithm:An overview[J]. Soft Computing, 2018, 22(2), 387-408.
    [19] Li M, Chen H, Wang X D, et al. An improved particle swarm optimization algorithm with adaptive inertia weights[J]. International Journal of Information Technology & Decision Making, 2019, 18(3):833-866.
    [20] Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(1):58-73.
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出版历程
  • 收稿日期:  2019-04-07
  • 修回日期:  2019-08-26
  • 刊出日期:  2020-05-26

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