| 一种多约束条件下的三脉冲交会优化设计方法 |
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| 引用本文： | 李君龙,李松洲,周荻. 一种多约束条件下的三脉冲交会优化设计方法[J]. 系统工程与电子技术, 2022, 44(8): 2612-2620. DOI: 10.12305/j.issn.1001-506X.2022.08.26 |
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| 作者姓名： | 李君龙 李松洲 周荻 |
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| 作者单位： | 1. 北京电子工程总体研究所, 北京 1008542. 哈尔滨工业大学航天学院, 黑龙江 哈尔滨 150001 |
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| 基金项目： | 国家自然科学基金(61773142) |
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| 摘 要： | 针对空间快速接近定点观测任务, 研究了具有交会时间和转移路径约束的多约束条件下的共面圆轨道间远距离三脉冲最优交会问题, 将Hill制导方法与粒子群算法相结合求解转移路径点以及转移时机的最优解。在求解过程中, 提出一种等价变换的方法, 将原始待求量转化为一组新的相互独立的待求变量, 将原始的各约束项转化为易描述和处理的搜索空间边界条件, 为完成算法的初始化过程带来了便利, 使得算法设计过程更为简洁。最后, 给出了两组三脉冲最优交会仿真实验, 仿真结果不仅验证了所提算法的有效性, 而且表明, 相对于常规的设置惩罚项处理约束的方法, 采用本文所提出的等价变换方法处理约束项后, 算法表现出更强大的搜索能力及更好的稳定性。
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| 关 键 词： | 三脉冲交会 Hill制导 粒子群优化 多约束 等价变换 |
| 收稿时间： | 2021-11-08 |
Optimization method for three-impulse rendezvous under multi-constraints |
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| Junlong LI,Songzhou LI,Di ZHOU. Optimization method for three-impulse rendezvous under multi-constraints[J]. System Engineering and Electronics, 2022, 44(8): 2612-2620. DOI: 10.12305/j.issn.1001-506X.2022.08.26 |
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| Authors: | Junlong LI Songzhou LI Di ZHOU |
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| Affiliation: | 1. Beijing Institute of Electronic System Engineering, Beijing 100854, China2. School of Astronautics, Harbin Institute of Technology, Harbin 150001, China |
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| Abstract: | The optimal three-impulse remote rendezvous between coplanar circles under multi-constraints including rendezvous time constraint and transfer path constraint is studied for the fast approaching and fixed-point observation mission in space. The Hill guidance and particle swarm optimization are combined to solve the optimal solution of transfer path point and transfer time. An equivalent transformation is proposed to transform the original variables to be solved into a new set of variables which are independent of each other. By applying this transformation algorithm, the original constraints are converted into the searching space boundaries which are easy to describe and cope with, such that it is convenient to perform the initialization and conduct the algorithm design. Finally, two simulation experiments of optimal three-impulse rendezvous are given. The results not only verify effectiveness of the proposed algorithm, but also indicate that compared with the conventional penalization method used to deal with constraints, the algorithm adopting the proposed equivalent transformation shows more powerful search ability and stronger stability. |
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| Keywords: | three-impulse rendezvous Hill guidance particle swarm optimization multi-constraint equivalent transformation |
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