系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (10): 3455-3463.doi: 10.12305/j.issn.1001-506X.2025.10.29

• 制导、导航与控制 • 上一篇    

融合机理与数据的陀螺飞轮姿态测量方法

赵昱宇, 刘耀蓬, 王雨潇   

  1. 中国民航大学电子信息与自动化学院,天津 300300
  • 收稿日期:2024-09-18 出版日期:2025-10-25 发布日期:2025-10-23
  • 通讯作者: 王雨潇
  • 作者简介:赵昱宇(1989—),女,副教授,博士,主要研究方向为姿态测控一体化、智能测控
    刘耀蓬(2002—),男,硕士研究生,主要研究方向为智能测控
  • 基金资助:
    国家自然科学基金(62003352,62003351);中央高校基本科研业务费(3122025041,3122025047)资助课题

Attitude measurement approach for gyrowheel based on integrating mechanism and data

Yuyu ZHAO, Yaopeng LIU, Yuxiao WANG   

  1. College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China
  • Received:2024-09-18 Online:2025-10-25 Published:2025-10-23
  • Contact: Yuxiao WANG

摘要:

针对陀螺飞轮非调谐大倾侧运行工况下姿态敏感机理复杂、测量精度低等问题,提出一种融合姿态测量机理与深度神经网络补偿的姿态测量方法。以陀螺飞轮的径向动力学模型为基础,在考虑内部传感器配置约束的前提下,基于简化假设构建机理测量模型。设计注意力机制与长短时记忆网络集成的深度学习回归模型,通过挖掘姿态测量误差与陀螺飞轮运行状态数据间的潜在关系实现测量误差补偿。提出基于机理与数据驱动的融合方法,使得陀螺飞轮以较高精度测量外部载体的角速度。通过对比实验验证了所提方法的测量精度提升效果和优越性能。

关键词: 陀螺飞轮, 姿态测量, 测量机理, 误差补偿, 深度学习

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

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