基于LSTM的空间机器人系统惯性张量在轨辨识

doi:10.7527/S1000-6893.2020.24615

Abstract

Abstract: 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.

Key words: space robots, Long-Short Term Memory (LSTM) network, inertia tensor, on-orbit identification, target capture

CLC Number:

V448.2

CHU Weimeng, YANG Jinzhao, WU Shu'nan, WU Zhigang. LSTM-based on-orbit identification of inertia tensor for space robot system[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(11): 524615-524615.

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

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