融合机理与数据的陀螺飞轮姿态测量方法

doi:10.12305/j.issn.1001-506X.2025.10.29

Abstract

Abstract:

Gyrowheel is always operating at de-tuned spin speed and large tilt angles, which leads to its complicated mechanism and low precision for attitude measurement. To solve this problem, an attitude measurement method is proposed that combines the attitude operating mechanism with the deep neural network-based error compensation. In consideration of the sensor configuration constraints of the gyrowheel system, a mechanism-based measurement model is constructed based on the transverse-axes dynamics through the simplification of assumptions. Besides, a deep learning-based regression model is developed by integrating a long short-term memory network and a dual-channel attention mechanism. It explores the potential relationship between the measurement errors and the operating status of the gyrowheel, yielding the error compensation for the mechanism-based model. On this basis, a hybrid attitude measurement approach is presented, which offers a high-precision way to estimate the external angular velocities of the gyrowheel. The precision improvement and superior performance are verified through comparative experiments.

Key words: gyrowheel, attitude measurement, measurement mechanism, error compensation, deep learning

CLC Number:

V 448.2

Yuyu ZHAO, Yaopeng LIU, Yuxiao WANG. Attitude measurement approach for gyrowheel based on integrating mechanism and data[J]. Systems Engineering and Electronics, 2025, 47(10): 3455-3463.

Figures/Tables 9

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References 31

1	TURAN E, SPERETTA S, GILL E. Autonomous navigation for deep space small satellites: scientific and technological advances[J]. Acta Astronautica, 2022, 193, 56- 74. doi: 10.1016/j.actaastro.2021.12.030
2	HAN C, XIONG W, XIONG M H, et al. Support vector regression-based operational effectiveness evaluation approach to reconnaissance satellite system[J]. Journal of Systems Engineering and Electronics, 2023, 34 (6): 1626- 1644. doi: 10.23919/JSEE.2023.000020
3	MÜLLER L, CHEN K K, MÖLLER G, et al. Real-time navigation solutions of low-cost off-the-shelf GNSS receivers on board the astrocast constellation satellites[J]. Advances in Space Research, 2024, 73 (1): 2- 19. doi: 10.1016/j.asr.2023.10.001
4	LEE D Y, PARK H, ROMANO M, et al. Development and experimental validation of a multi-algorithmic hybrid attitude determination and control system for a small satellite[J]. Aerospace Science and Technology, 2018, 78, 494- 509. doi: 10.1016/j.ast.2018.04.040
5	MANSOURI A, ALEM-TABRIZ A. Redundancy allocation optimizing in the satellite attitude determination and control system based on the exact solution algorithm[J]. Communications in Statistics-Theory and Methods, 2024, 53 (4): 1516- 1534. doi: 10.1080/03610926.2022.2104873
6	王平, 王华, 任元. 基于磁悬浮转子微框架能力的航天器姿态二自由度测控一体化控制方法[J]. 系统工程与电子技术, 2016, 38 (7): 1614- 1622. doi: 10.3969/j.issn.1001-506X.2016.07.21
	WANG P, WANG H, REN Y. Spacecraft attitude integration method of measurement and control in two degrees of freedom based on micro framework ability of magnetically suspended rotor[J]. Systems Engineering and Electronics, 2016, 38 (7): 1614- 1622. doi: 10.3969/j.issn.1001-506X.2016.07.21
7	CHEN S, HUO X, ZHAO H, et al. Active tilting flutter suppression of gyrowheel with composite-structured adaptive compensator[J]. IEEE Trans. on Industrial Electronics, 2021, 68 (7): 6227- 6237. doi: 10.1109/TIE.2020.2994858
8	FANG J C, ZHENG S Q, HAN B C. Attitude sensing and dynamic decoupling based on active magnetic bearing of MSDGCMG[J]. IEEE Trans. on Instrumentation and Measurement, 2012, 61 (2): 338- 348. doi: 10.1109/TIM.2011.2164289
9	STALEY D A, TYC G, FRIESE P R. System and method for spacecraft attitude control[P]. US: US20020040950A1, 2002-04-11.
10	HALL J M. Calibration of an innovative rate sensing/momentum management instrument for de-tuned operation and temperature effects[D]. Ottawa: Carleton University, 2008.
11	GUO J, ZHONG M Y. Calibration and compensation of the scale factor errors in DTG POS[J]. IEEE Trans. on Instrumentation and Measurement, 2013, 62 (10): 2784- 2794. doi: 10.1109/TIM.2013.2261631
12	赵砚驰, 程建华, 赵琳. 惯性导航系统陀螺仪的发展现状与未来展望[J]. 导航与控制, 2020, 19 (Z1): 189- 196.
	ZHAO Y C, CHENG J H, ZHAO L. Development status and future prospects of gyroscope in inertial navigation[J]. Navigation and Control, 2020, 19 (Z1): 189- 196.
13	YU C M, CAI Y W, REN Y, et al. A full-frequency angular rate measurement method of spacecraft based on multi-MSCSG weight assignment[J]. IEEE Sensors Journal, 2023, 23 (8): 8304- 8313. doi: 10.1109/JSEN.2023.3258442
14	张晗, 张宇, 赵辉. 陀螺飞轮动力学特征分析[J]. 系统仿真学报, 2015, 27 (1): 112- 117.
	ZHANG H, ZHANG Y, ZHAO H. Dynamics analysis of gyrowheel for its micro-spacecraft attitude control application[J]. Journal of System Simulation, 2015, 27 (1): 112- 117.
15	OWER J C. Analysis and control system design of an innovative tuned-rotor instrument[D]. Ottawa: Carleton University, 2000.
16	LIU X K, ZHAO H, YAO Y, et al. Modeling and analysis of micro-spacecraft attitude sensing with gyrowheel[J]. Sensors, 2016, 16 (8): 1321. doi: 10.3390/s16081321
17	LIU X K, YAO Y, MA K M, et al. Spacecraft angular rates estimation with gyrowheel based on extended high gain observer[J]. Sensors, 2016, 16 (4): 537. doi: 10.3390/s16040537
18	ZHAO Y Y, ZHAO H, HUO X, et al. A novel external rate sensing algorithm for gyrowheel based on Jacobi-Anger expansion[J]. IEEE Sensors Journal, 2019, 19 (16): 6662- 6674. doi: 10.1109/JSEN.2019.2912390
19	CHEN S, HUO X, ZHAO H, et al. Axial unbalance identification of gyrowheel rotor based on multi-position calibration and CEEMDAN-IIT denoising[J]. Measurement, 2021, 183, 109852. doi: 10.1016/j.measurement.2021.109852
20	ZHAO Y Y, ZHAO H, HUO X, et al. A novel two-step parameter identification method for gyrowheel system[J]. Measurement, 2019, 136, 367- 381. doi: 10.1016/j.measurement.2018.12.082
21	KIKUYA Y, OHTA K, IWASAKI Y, et al. Development and in-orbit operation of deep learning attitude sensor[J]. Journal of Spacecraft and Rockets, 2023, 60 (5): 1400- 1409. doi: 10.2514/1.A35388
22	SOUFI O, BELOUADHA F Z. An intelligent deep learning approach to spacecraft attitude control: the case of satellites[J]. Journal of the Franklin Institute-Engineering and Applied Mathematics, 2024, 361 (14): 107078. doi: 10.1016/j.jfranklin.2024.107078
23	MOHAMED M I, BADRAN K M, HUSSIEN A E. Anomaly detection for agile satellite attitude control system using hybrid deep-learning technique[J]. Journal of Aerospace Information Systems, 2023, 20 (12): 890- 904.
24	程向红, 吴昕怡, 刘丰宇, 等. 基于改进LSTM网络的无人机MEMS-IMU零偏在线标定方法[J]. 中国惯性技术学报, 2024, 32 (3): 213- 218.
	CHENG X H, WU X Y, LIU F Y, et al. An improved LSTM neural network online calibration method of MEMS-IMU bias for UAV[J]. Journal of Chinese Inertial Technology, 2024, 32 (3): 213- 218.
25	CHEN Y D, JIANG W, WANG J, et al. An LSTM-assisted GNSS/INS integration system using IMU recomputed error information for train localization[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (3): 2658- 2671. doi: 10.1109/TAES.2023.3328318
26	GOLROUDBARI A A, SABOUR M H. Generalizable end-to-end deep learning frameworks for real-time attitude estimation 6DoF inertial measurement units[J]. Measurement, 2023, 217, 113105. doi: 10.1016/j.measurement.2023.113105
27	LIN C Y, ZHEN R, TONG Y T, et al. Regional ship collision risk prediction: an approach based on encoder-decoder LSTM neural network model[J]. Ocean Engineering, 2024, 296, 117019. doi: 10.1016/j.oceaneng.2024.117019
28	LE-DUC T, NGUYEN-XUAN H, LEE J. Sequential motion optimization with short-term adaptive moment estimation for deep learning problems[J]. Engineering Applications of Artificial Intelligence, 2024, 129, 107593. doi: 10.1016/j.engappai.2023.107593
29	ZHENG Y, CHEN C X, WANG R Q, et al. Stability analysis of rock slopes subjected to block-flexure toppling failure using adaptive moment estimation method （Adam）[J]. Rock Mechanics and Rock Engineering, 2022, 55 (6): 3675- 3686. doi: 10.1007/s00603-022-02828-5
30	刘晓坤. 基于陀螺飞轮的航天器姿态角速度测量方法研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.
	LIU X K. Study on spacecraft attitude angular velocity measurement method based on gyrowheel[D]. Harbin: Harbin Institute of Technology, 2018.
31	ZHAO Q, YAO Y, LIU X K, et al. Modeling and analysis of gyrowheel with friction and dynamic unbalance[C]// Proc. of the Asian Simulation Conference, 2016: 262−271.

参数	设计值	真实值
I_rt/（kg·m²）	1.11×10⁻³	1.01×10⁻³
I_rs/（kg·m²）	1.96×10⁻³	1.94×10⁻³
I_gt/（kg·m²）	1.61×10⁻⁵	1.62×10⁻⁵
I_gs/（kg·m²）	2.39×10⁻⁵	2.40×10⁻⁵
k_g/（N·m/rad）	5.60×10⁻¹	5.50×10⁻¹
c_g/（ N·m/（rad/s））	6.54×10⁻⁴	6.65×10⁻⁴

参数	设置值
传感器维数	5
时间窗长度	8
编码器隐层单元数	30
解码器隐层单元数	30
回归模块隐层节点数	50
输出维数	1

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Attitude measurement approach for gyrowheel based on integrating mechanism and data

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