基于脉冲初值的小推力转移轨道优化研究
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摘要
针对小推力转移轨道优化过程往往忽略初值多样性的现状,研究了基于不同脉冲初值的小推力转移轨道优化问题。基于直接法的离散思想建立了小推力转移轨道优化模型,提出了基于粒子群和序列二次规划的组合优化算法,以地球1∶1共振近地小行星2016HO3交会任务为例,将3种典型的脉冲轨道作为初值设计了燃料最优小推力转移轨道。仿真结果表明:3种初值轨道优化得到了2个小推力转移发射窗口,两者燃料消耗差距不超过6%。不同的初值对小推力轨道的整体性能指标影响较小,但开关机时刻和推力方向的变化会产生较大差异,从而得到不同的最优控制曲线。
Considering the fact that the diversity of initial values is often neglected in the optimization process of the low-thrust transfer trajectory, the low-thrust transfer trajectory optimization with different initial impulse values is studied in this paper. Firstly, a low-thrust transfer trajectory optimization model based on the direct method is established. Then, a combinatorial optimization algorithm based on particle swarm optimization and sequential quadratic programming is proposed. Finally, the asteroid 2016 HO3 rendezvous mission is selected as a simulation example, and the optimization results of the low-thrust transfer orbit are analyzed with single-revolution double-pulse, triple-pulse and multi-revolution double-pulse as initial values. The results show that two launch windows are obtained from three initial values, with the fuel consumption gap of less than 6%. Different initial value inputs will cause large differences in switch time and the change of thrust direction, but only a subtle distinction in overall performance indexes, thus different optimal control curves can be obtained.
Considering the fact that the diversity of initial values is often neglected in the optimization process of the low-thrust transfer trajectory, the low-thrust transfer trajectory optimization with different initial impulse values is studied in this paper. Firstly, a low-thrust transfer trajectory optimization model based on the direct method is established. Then, a combinatorial optimization algorithm based on particle swarm optimization and sequential quadratic programming is proposed. Finally, the asteroid 2016 HO3 rendezvous mission is selected as a simulation example, and the optimization results of the low-thrust transfer orbit are analyzed with single-revolution double-pulse, triple-pulse and multi-revolution double-pulse as initial values. The results show that two launch windows are obtained from three initial values, with the fuel consumption gap of less than 6%. Different initial value inputs will cause large differences in switch time and the change of thrust direction, but only a subtle distinction in overall performance indexes, thus different optimal control curves can be obtained.
引文
[1] 朱政帆, 高扬. 空间小推力最优Bang-Bang控制的两类延拓解法综述[J]. 深空探测学报, 2017, 4(2):101-110.
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[6] 蒋小勇, 张洪波, 汤国建. 基于多冲量能耗估算的小推力任务窗口搜索[J].中国空间科学技术, 2014, 34(4):1-7.
[7] 李俊峰, 蒋方华. 连续小推力航天器的深空探测轨道优化方法综述[J].力学与实践, 2011, 33(3):1-6.
[8] HARGRAVES C R, PARIS S W. Direct trajectory optimization using nonlinear programming and collocation[J]. Journal of Guidance, Control, and Dynamics, 1987, 10(4):338-342.
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[10] GILL P E, MURRAY W, SAUNDERS M A. SNOPT: an SQP algorithm for large-scale constrained optimization[J]. Siam Review, 2005, 47(1):99-131.
[11] JIANG F, BAO Y H, LI J. Practical techniques for low-thrust trajectory optimization with homotopic approach[J]. Journal of Guidance, Control, and Dynamics, 2012, 35(1):245-258.
[2] FOGEL J, KOBLICK D, SHAH N. Survey of low-thrust gravity assist trajectory optimization methods, with comparisons to a novel, multi-impulse discretization approach[C]//International Astronautical Congress. 2015:168-171.
[3] KOWALKOWSKI T D, KANGAS J A, PARCHER D W. Jupiter icy moons orbiter interplanetary injection period analysis[C]//AAS/AIAA Spaceflight Mechanics Meeting. 2006:131-135.
[4] 陈杨, 宝音贺西, 李俊峰. 我国小行星探测目标分析与电推进轨道设计[J]. 中国科学:物理学力学天文学, 2011, 41(9):1104-1111.
[5] 李九天. 多任务深空探测轨道设计优化方法研究[D]. 长沙:国防科学技术大学, 2013.
[6] 蒋小勇, 张洪波, 汤国建. 基于多冲量能耗估算的小推力任务窗口搜索[J].中国空间科学技术, 2014, 34(4):1-7.
[7] 李俊峰, 蒋方华. 连续小推力航天器的深空探测轨道优化方法综述[J].力学与实践, 2011, 33(3):1-6.
[8] HARGRAVES C R, PARIS S W. Direct trajectory optimization using nonlinear programming and collocation[J]. Journal of Guidance, Control, and Dynamics, 1987, 10(4):338-342.
[9] 周誌元,谭天乐.小行星探测器轨迹优化方法[J]. 上海航天, 2014, 31(2):57-64.
[10] GILL P E, MURRAY W, SAUNDERS M A. SNOPT: an SQP algorithm for large-scale constrained optimization[J]. Siam Review, 2005, 47(1):99-131.
[11] JIANG F, BAO Y H, LI J. Practical techniques for low-thrust trajectory optimization with homotopic approach[J]. Journal of Guidance, Control, and Dynamics, 2012, 35(1):245-258.