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.
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
.
DOI: 10.7527/S1000-6893.2020.24615
[1] FLORES-ABAD A, MA O, PHAM K, et al. A review of space robotics technologies for on-orbit servicing[J]. Progress in Aerospace Sciences, 2014, 68:1-26.
[2] MENG Q L, LIANG J X, MA O. Identification of all the inertial parameters of a non-cooperative object in orbit[J]. Aerospace Science and Technology, 2019, 91:571-582.
[3] BIONDI G, MAURO S, MOHTAR T, et al. Attitude recovery from feature tracking for estimating angular rate of non-cooperative spacecraft[J]. Mechanical Systems and Signal Processing, 2017, 83:321-336.
[4] MUROTSU Y, SENDA K, OZAKI M, et al. Parameter identification of unknown object handled by free-flying space robot[J]. Journal of Guidance, Control, and Dynamics, 1994, 17(3):488-494.
[5] 王明, 黄攀峰, 常海涛, 等. 基于机械臂运动的组合航天器惯性参数在轨辨识[J]. 西北工业大学学报, 2014, 32(5):811-816. WANG M, HUANG P F, CHANG H T, et al. On-orbit identification of inertia parameters of compound spacecraft using space manipulator[J]. Journal of Northwestern Polytechnical University, 2014, 32(5):811-816(in Chinese).
[6] 侯振东, 王兆魁, 张育林. 基于推力器的组合航天器质量特性辨识方法研究[J]. 航天控制, 2015, 33(1):54-60. HOU Z D, WANG Z K, ZHANG Y L. Research on identification of mass characteristics for spacecraft combination based on thrusters[J]. Aerospace Control, 2015, 33(1):54-60(in Chinese).
[7] 张博, 梁斌, 王学谦, 等. 基于自适应反作用零空间控制的大型非合作目标动力学参数实时辨识仿真[J]. 机器人, 2016, 38(1):98-106. ZHANG B, LIANG B, WANG X Q, et al. Simulation of real-time dynamic parameter identification for large non-cooperative targets using adaptive reaction null space control[J]. Robot, 2016, 38(1):98-106(in Chinese).
[8] CHU Z Y, MA Y, HOU Y Y, et al. Inertial parameter identification using contact force information for an unknown object captured by a space manipulator[J]. Acta Astronautica, 2017, 131:69-82.
[9] XU W F, HU Z H, ZHANG Y, et al. On-orbit identifying the inertia parameters of space robotic systems using simple equivalent dynamics[J]. Acta Astronautica, 2017, 132:131-142.
[10] PUNJANI A, ABBEEL P. Deep learning helicopter dynamics models[C]//2015 IEEE International Conference on Robotics and Automation (ICRA). Piscataway:IEEE Press, 2015:3223-3230.
[11] AULI M, GALLEY M, QUIRK C, et al. Joint language and translation modeling with recurrent neural networks[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2013, 3:1044-1054.
[12] LIU Y H, WANG H L, SU Z K, et al. Deep learning based trajectory optimization for UAV aerial refueling docking under bow wave[J]. Aerospace Science and Technology, 2018, 80:392-402.
[13] ZHAO R, YAN R Q, WANG J J, et al. Learning to monitor machine health with convolutional bi-directional LSTM networks[J]. Sensors, 2017, 17(2):273.
[14] DONG Y Q. An application of Deep Neural Networks to the in-flight parameter identification for detection and characterization of aircraft icing[J]. Aerospace Science and Technology, 2018, 77:34-49.
[15] 魏晓良, 潮群, 陶建峰, 等. 基于LSTM和CNN的高速柱塞泵故障诊断[J]. 航空学报, 2021, 42(3):423876. WEI X L, CHAO Q, TAO J F, et al. Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(3):423876(in Chinese).
[16] YU W H, TAN J, LIU C K, et al. Preparing for the unknown:Learning a universal policy with online system identification[DB/OL]. arXiv preprint:1702.02453, 2017.
[17] TOBIN J, FONG R, RAY A, et al. Domain randomization for transferring deep neural networks from simulation to the real world[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway:IEEE Press, 2017:23-30.
[18] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[J]. Journal of Machine Learning Research, 2011, 15:315-323.
[19] HE K M, ZHANG X Y, REN S Q, et al. Delving deep into rectifiers:surpassing human-level performance on ImageNet classification[C]//2015 IEEE International Conference on Computer Vision (ICCV). Piscataway:IEEE Press, 2015:1026-1034.
[20] KINGMA D, BA J. Adam:a method for stochastic optimization[DB/OL]. arXiv preprint:1412.6980, 2014.
[21] CHAI R Q, TSOURDOS A, SAVVARIS A, et al. Real-time reentry trajectory planning of hypersonic vehicles:A two-step strategy incorporating fuzzy multiobjective transcription and deep neural network[J]. IEEE Transactions on Industrial Electronics, 2020, 67(8):6904-6915.
CJA