Deep learning-based inertia tensor identification of the combined spacecraft

被引:6
|
作者
Chu, Weimeng [1 ]
Wu, Shunan [1 ]
He, Xiao [1 ]
Liu, Yufei [2 ]
Wu, Zhigang [3 ]
机构
[1] Dalian Univ Technol, Sch Aeronaut & Astronaut, Dalian, Peoples R China
[2] China Acad Space Technol, Qian Xuesen Lab Space Technol, Beijing, Peoples R China
[3] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian, Peoples R China
基金
中国国家自然科学基金;
关键词
Combined spacecraft; inertia tensor; identification; deep learning; deep neural network; PARAMETER-IDENTIFICATION; NEURAL-NETWORKS; MODEL; TRACKING;
D O I
10.1177/0954410020904555
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The identification accuracy of inertia tensor of combined spacecraft, which is composed by a servicing spacecraft and a captured target, could be easily affected by the measurement noise of angular rate. Due to frequently changing operating environments of combined spacecraft in space, the measurement noise of angular rate can be very complex. In this paper, an inertia tensor identification approach based on deep learning method is proposed to improve the ability of identifying inertia tensor of combined spacecraft in the presence of complex measurement noise. A deep neural network model for identification is constructed and trained by enough training data and a designed learning strategy. To verify the identification performance of the proposed deep neural network model, two testing set with different ranks of measure noises are used for simulation tests. Comparison tests are also delivered among the proposed deep neural network model, recursive least squares identification method, and tradition deep neural network model. The comparison results show that the proposed deep neural network model yields a more accurate and stable identification performance for inertia tensor of combined spacecraft in changeable and complex operating environments.
引用
收藏
页码:1356 / 1366
页数:11
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