基于响应面法的月球着陆器软着陆性能优化

王家俊; 王春洁; 宋顺广

doi:10.13700/j.bh.1001-5965.2013.0364

基于响应面法的月球着陆器软着陆性能优化

doi: 10.13700/j.bh.1001-5965.2013.0364

北京航空航天大学机械工程及自动化学院, 北京 100191

详细信息

作者简介:
王家俊（1990- ），男，河北定州人，硕士生，wangjiajun@me.buaa.edu.cn.

中图分类号: V11
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出版历程
- 收稿日期: 2013-06-25
- 网络出版日期: 2014-05-20

Performance optimization of lunar lander based on response surface methodology

School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 为了提高月球着陆器软着陆性能，建立了月球着陆器的多体动力学参数化模型.对某典型恶劣工况进行了优化拉丁超立方实验设计，辨识关键实验因子.利用参数化模型和实验设计获取样本点构造了响应面模型，并检验了响应面的拟合精度.以多个动力学性能作为优化目标，用响应面计算结果构造目标函数和约束条件，对响应面参数做多目标优化.将优化所得参数代入动力学模型中进行仿真，优化效果明显，验证了基于响应面法的软着陆性能优化方法的可行性，为今后的仿真实验提供参考.
- 月球着陆器 /
- 参数化模型 /
- 实验设计 /
- 响应面 /
- 多目标优化
Abstract: The parametric model of the lunar lander based on multi-body dynamics was established for improving landing performances of the lunar lander. With the model, an optimal Latin hypercube experimental design for a typical failure case was carried out to find out the key factors. The sample points were acquired by using parametric model and the design of experiments to construct the response surface model, and the response surface fitting accuracy was tested. The calculated response surface results were taken as the objective functions and constraint conditions, and response surface parameters were optimized to improve the comprehensive landing performances. The optimized model was then obtained by substituting the result of the multi-objective optimization into the parametric model. Compared with the previous model, the optimized model has more excellent performance on the typical case. So that the feasibility of optimization method was verified, which can be used for the future simulation experiments.
- lunar lander /
- parameterized model /
- design of experiments /
- response surface /
- multi-objective optimization

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参考文献(12)

[1]	曾福明, 杨建中,满剑锋,等.月球着陆器着陆缓冲机构设计方法研究[J].航天器工程,2011,20(2):46-51 Zeng Fuming,Yang Jianzhong,Man Jianfeng,et al.Study of design method of landing gear for lunar lander[J].Spacecraft Engineering,2011,20(2):46-51(in Chinese)
[2]	逯运通, 宋顺广,王春洁,等.基于刚柔耦合模型的月球着陆器动力学分析[J].北京航空航天大学学报,2010,36(11):1348-1352 Lu Yuntong,Song Shunguang,Wang Chunjie,et al.Dynamic analysis for lunar lander based on rigid-flexible coupled model[J].Journal of Beijing University of Aeronautics and Astronautics,2010,36(11):1348-1352(in Chinese)
[3]	Hilderman R A, Mueller W H,Mantus M.Landing dynamics of the lunar excursion module[J].Journal of Spacecraft and Rockets,1966,3(10):1484-1489
[4]	朱汪,杨建中. 月球探测器软着陆机构着陆腿模型与仿真分析[J].宇航学报,2008,29(6):1723-1728 Zhu Wang,Yang Jianzhong.Modeling and simulation of landing leg for the lunar landing gear system[J].Journal of Astronautics,2008,29(6):1723-1728(in Chinese)
[5]	Lavender R E. A Monte Carlo approach to touchdown dynamics for soft lunar landings[R].NASA-TN-D-3117,1965
[6]	Lu Y T, Wang C J,Zeng F M,et al.A Monte Carlo analysis of lunar lander dynamics at touchdown[C]//CSAA.Proceedings of 2010 Asia-Pacific International Symposium on Aerospace Technology.Xi'an:Northwestern Polytechnical University Press,2010:481-484
[7]	Marler R T, Arora J S.Survey of multi-objective optimization methods for engineering[J].Structural and Multidisciplinary Optimization,2004,26(6):369-395
[8]	刘晓路,陈英武, 荊显荣,等.优化拉丁方试验设计方法及其应用[J].国防科技大学学报,2011,33(5):73-77 Liu Xiaolu,Chen Yingwu,Jing Xianrong,et al.Optimized latin hypercube sampling method and its application[J].Journal of National University of Defence Technology,2011,33(5):73-77(in Chinese)
[9]	Khuri A I, Mukhopadhyay S.Response surface methodology[J].WIREs Computational Statistics,2010,2(2):128-149
[10]	方开泰,全辉, 陈庆云.实用回归分析[M].北京:科学出版社,1988:29-35,151-152 Fang Kaitai,Quan Hui,Chen Qingyun.Practical regression analysis[M].Beijing:Science Press,1988:29-35,151-152(in Chinese)
[11]	Srinivas N, Deb K.Multiobjective optimization using nondominated sorting in genetic algorithms[J].Evolutionary Computation,1994,2(3):221-248
[12]	Deb K, Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197