触地关机模式下的着陆器软着陆稳定性研究

董洋; 王春洁; 吴宏宇; 丁宗茂; 满剑锋

doi:10.13700/j.bh.1001-5965.2018.0318

触地关机模式下的着陆器软着陆稳定性研究

doi: 10.13700/j.bh.1001-5965.2018.0318

董洋¹,
王春洁^{1, 2, ,},
吴宏宇¹,
丁宗茂¹,
满剑锋¹

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 100083

基金项目:

国家自然科学基金 51635002

详细信息

作者简介:
董洋男, 博士研究生。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:数字化设计与仿真分析

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: 2018-06-03
- 录用日期: 2018-09-03
- 网络出版日期: 2019-02-20

Soft landing stability of lander in mode of shutdown at touchdown

1.
School of Mechanical Engineering & Automation, Beihang University, Beijing 100083, China
2.
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100083, China

Funds:

National Natural Science Foundation of China 51635002

More Information

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

摘要

摘要:
以触地关机软着陆模式下的某型着陆器为研究对象，建立其软着陆过程的动力学仿真模型。基于仿真模型，结合优化方法与多岛遗传算法（MIGA）确定了着陆器的极恶劣地形工况参数，并利用径向基函数（RBF）神经网络建立了反映极恶劣工况下着陆器速度参数与稳定性指标值之间映射关系的代理模型。将着陆器速度参数做离散化处理得到样本点，利用神经网络模型计算了各样本点对应的软着陆稳定性指标值，基于计算结果给出了各项软着陆稳定性指标的云图和三维速度稳定性边界，并得到了综合各项稳定性指标的着陆器速度稳定性边界。分析结果可直观地确定保证着陆器安全着陆的速度取值范围，为着陆器速度的合理控制提供参考。
- 腿式着陆器 /
- 触地关机 /
- 软着陆 /
- 径向基函数(RBF)神经网络 /
- 稳定性边界
Abstract:
A lander in the soft landing mode of shutdown at touchdown is taken as the research object, and the dynamic simulation model of its soft landing process is established. Based on the simulation model, the parameters of the worst landing conditions are obtained by combining optimization method and multi-island genetic algorithm (MIGA). The radial basis function (RBF) neural network is used to establish a surrogate model which reflects the mapping relationship between the lander's velocity parameter and the value of stability indicator. The sample points are obtained by discretizing the velocity parameters of the lander, and the soft landing stability performance of each sample point are calculated by using the neural network model. Based on the calculation results, the contour and the three-dimensional velocity stability boundary of each soft landing stability indicator are given, and then a comprehensive stability boundary of lander velocity is obtained. The analysis results can intuitively determine the range of velocity for safe landing, which provides reference for the rational control of lander velocity.
- leg type lander /
- shutdown at touchdown /
- soft landing /
- radial basis function (RBF) neural network /
- stability boundary

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图 1 着陆缓冲机构

Figure 1. Landing buffer mechanism

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图 2 缓冲杆等效模型

Figure 2. Equivalent model of buffer rod

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图 3 缓冲杆力学特性

Figure 3. Mechanical behavior of buffer rod

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图 4 缓冲元件力学特性

Figure 4. Mechanical behavior of cushion element

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图 5 坐标系定义及发动机位置

Figure 5. Coordinate system definition and engine position

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图 6 主发动机推力变化过程

Figure 6. Thrust changing process of main engine

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图 7 部分着陆工况参数示意图

Figure 7. Schematic diagram of partial landing conditions parameters

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图 8 不同v_x下的α_em、D_SM、L_D及H_M云图

Figure 8. Contour of α_em, D_SM, L_D and H_M under different v_x

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图 9 各指标下着陆速度三维稳定性边界

Figure 9. 3D stability boundary determined by landing velocity concerning different indicators

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图 10 综合指标下着陆速度三维稳定性边界

Figure 10. 3D stability boundary determined by landing velocity considering comprehensive indicators

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图 11 不同v_x下综合稳定性边界

Figure 11. Comprehensive stability boundary under different v_x

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图 12 v_x-v_h综合稳定性边界

Figure 12. v_x-v_h comprehensive stability boundary

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表 1 优化参数设置

Table 1. Setting of optimal parameters

参数	数值
岛屿种群规模	10
岛数	10
进化代数	25
交叉概率	0.9
迁移概率	0.01
迁移间隔	4

下载: 导出CSV

表 2 极恶劣工况

Table 2. Worst landing conditions

指标	最优解X^*=[α, K, ψ, β]^T
max α_em	[3.713, 1, 0.624, 174.35]^T
max D_SM	[1.995, 0, 17.32, 138.89]^T
min L_D	[12.00, 1, 35.51, 290.63]^T
min H_M	[0.405, 0, 13.98, 226.51]^T
注：α、ψ、β单位为(°)；K无量纲。

下载: 导出CSV

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