基于多工况的新型着陆器软着陆性能优化

吴宏宇; 王春洁; 丁建中; 满剑锋; 罗敏

doi:10.13700/j.bh.1001-5965.2016.0296

基于多工况的新型着陆器软着陆性能优化

doi: 10.13700/j.bh.1001-5965.2016.0296

吴宏宇¹,
王春洁^{1, 2, ,},
丁建中¹,
满剑锋^{1, 3},
罗敏³

1.
北京航空航天大学机械工程及自动化学院, 北京 100083
2.
北京航空航天大学虚拟现实技术与系统国家重点实验室, 北京 10008
3.
北京空间飞行器总体设计部, 北京 100094

详细信息

作者简介:
吴宏宇, 男, 硕士研究生。主要研究方向:航天机构的设计与动力学分析

王春洁, 女, 博士, 教授, 博士生导师。主要研究方向:机械设计及理论

通讯作者:
王春洁, E-mail:wangcj@buaa.edu.cn

中图分类号: V423.6
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- 被引次数: 0
出版历程
- 收稿日期: 2016-04-14
- 录用日期: 2016-05-13
- 网络出版日期: 2017-04-20

Soft landing performance optimization for novel lander based on multiple working conditions

WU Hongyu¹,
WANG Chunjie^{1, 2
, ,},
DING Jianzhong¹,
MAN Jianfeng^{1, 3},
LUO Min³

1.
School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2.
State Key Laboratory of Virtual Reality and Systems, Beijing University of Aeronautics and Astronautics, Beijing 10008
3.
Beijing Institute of Spacecraft System Engineering, Beijing 100094, China

More Information

Corresponding author: WANG Chunjie, E-mail: wangcj@buaa.edu.cn

摘要

摘要:
以新型腿式着陆器为研究对象，建立其刚柔耦合动力学分析模型，实现着陆器软着陆过程的仿真。通过仿真计算，确定着陆器最易翻倒、底面最易与星球表面岩石碰撞、主体承受最大碰撞力的3组恶劣着陆工况。分析着陆器缓冲机构构型选取设计变量，基于仿真得到的3组恶劣工况，应用第二代非劣排序遗传算法（NSGA-Ⅱ）实现着陆器软着陆性能的优化，优化目标为增强着陆器抗翻倒能力、降低着陆器底面与星球表面岩石碰撞的可能性、降低着陆器主体最大受力值。将优化所得参数代入模型重新进行仿真，着陆器不再发生翻倒，着陆平台底面与星球表面最小距离提高4.2%，主体最大受力值降低12.1%。
- 腿式着陆器 /
- 刚柔耦合模型 /
- 动力学分析 /
- 软着陆性能 /
- 多目标优化
Abstract:
A rigid-flexible coupled dynamics model was established for simulating and analyzing the soft landing process of the novel leg type lander. Three groups of bad landing conditions, in which the lander most easily overturns, bottom surface of the lander most easily collides with rocks on the surface of the planet, and body of the lander bears the greatest impact force, were found by iterating over landing parameters. According to the configuration of buffering mechanism, design variables of optimization were selected. Based on the three groups of bad landing conditions and non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), a multi-objective optimization method was applied to enhance the ability of the lander to resist overturning, reduce the possibility that the bottom surface of lander collides with rocks on the surface of the planet, and reduce the impact on the body of lander. In the simulation using optimized parameters, the model does not overturn any longer. The minimum distance between bottom surface of the landing platform of the lander and surface of the planet increases by 4.2%, and the impact on the body of lander reduces by 12.1%.
- leg type lander /
- rigid-flexible coupled model /
- dynamical analysis /
- soft landing performance /
- multi-objective optimization

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图 1 着陆缓冲机构

Figure 1. Landing buffer mechanism

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图 2 辅助支柱缓冲机构及力学特性

Figure 2. Buffer mechanism and mechanical behavior of assistant pillar

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图 3 外部缓冲件等效方法

Figure 3. Equivalent method of external buffer

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图 4 外部缓冲件力学特性

Figure 4. Mechanical behavior of external buffer

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图 5 着陆工况的参数

Figure 5. Parameters of landing condition

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图 6 着陆器性能分析

Figure 6. Performance analysis of lander

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图 7 多目标优化流程图

Figure 7. Flowchart of multi-objective optimization

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表 1 着陆工况各参数取值

Table 1. Value of parameters of landing condition

参数	取值
μ	0.3，0.8
α/(°)	7
n	0，1，2，3
v_x/(m·s^-1)	3.5
v_z/(m·s^-1)	-1，+1
θ_p/(°)	0~45
θ_f/(°)	-3，+3
m/kg	700，800

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表 2 极限工况计算结果

Table 2. Calculation results of the worst landing condition

极限指标值	μ	n	v_z/(m·s^-1)	θ_p/(°)	θ_f/(°)	m/kg
L_T=1.11 mm	0.8	2	1	9	3	700
L_H=734.23 mm	0.3	3	-1	0	-3	800
F_M=27.22 kN	0.8	3	-1	0	3	800

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表 3 设计变量取值范围

Table 3. Value range of design variables

设计变量	取值范围
k_x	1~2
k_z	1~1.5
d_z/m	1.37~1.45

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表 4 优化参数取值^[11]

Table 4. Value of optimal parameters^[11]

参数	种群规模	进化代数	交叉指数	变异指数	交叉概率
取值	12	20	10	20	0.9

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表 5 帕累托最优解集

Table 5. Pareto optimal solution aggregate

编号	k_z	k_x	d_z/m	L_H/mm	F_M/kN	L_T/mm
1	1.46	1.07	1.45	757.91	24.07	413.92
2	1.46	1.23	1.45	757.97	26.31	416.59
3	1.49	1.13	1.44	761.38	25.57	378.00
4	1.49	1.03	1.43	763.07	23.36	349.08
5	1.49	1.05	1.41	765.81	23.95	258.71
6	1.49	1.22	1.42	764.49	27.31	306.33
7	1.49	1.08	1.43	762.92	24.07	352.49
8	1.49	1.04	1.43	762.57	23.50	360.83
9	1.49	1.03	1.42	764.15	23.46	311.13
10	1.48	1.19	1.43	761.46	25.72	365.11

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表 6 优化前后性能对比

Table 6. Comparison of performance before and after optimization

对比项	k_x	k_z	d_z/m	L_T/mm	L_H/mm	F_M/kN
优化前	1	1	1.36	1.11	734.23	27.22
优化后	1.05	1.49	1.41	234.36	765.81	23.95

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