LSTM-based on-orbit identification of inertia tensor for space robot system

被引：0
|
作者：
Chu W. [1 ]
Yang J. [1 ]
Wu S. [1 ]
Wu Z. [2 ]
机构：
[1] School of Aeronautics and Astronautics, Dalian University of Technology, Dalian
[2] State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian
来源：
Wu, Zhigang (wuzhg@dlut.edu.cn) | 1600年 / Chinese Society of Astronautics卷 / 42期
基金：
中国国家自然科学基金;
关键词：
Inertia tensor; Long-Short Term Memory (LSTM) network; On-orbit identification; Space robots; Target capture;
D O I：
10.7527/S1000-6893.2020.24615
中图分类号：
学科分类号：
摘要：
The system inertia tensor of the space robot is time-varying in the process of an out-of-control target capture and even undergoes abrupt changes at the moment of capture, seriously affecting the accuracy of its overall attitude control. To address the above problem, we propose an on-orbit real-time identification method for the system inertia tensor based on Long-Short Term Memory (LSTM). According to the two stages of pre-capture and post-capture, the dynamic model of the space robot is firstly developed using the Lagrangian equation. Based on the proposed model, the domain randomization method is then adopted to generate sufficient training data to train the parameter identification network constructed by an LSTM network and a multilayer fully connected network. Finally, the trained parameter identification network is used to identify the system inertia tensor. The test results demonstrate that the proposed method can accurately identify the system inertia tensor during the capture process of the space robot. The average relative identification error of the main moment of inertia is less than 0.001, and that of the product of inertia less than 0.01. © 2021, Beihang University Aerospace Knowledge Press. All right reserved.
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