Modeling and Prediction of Thermal Deformation Errors in Fiber Optic Gyroscopes Based on the TD-Model

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 23; p. 9450
• Main Authors: Xu, Jintao, Tian, Ailing, Liu, Hui, Liu, Ying
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.11.2023
• Subjects: Accuracy; Algorithms; Analysis; biased regression; Deformation; Equipment and supplies; fiber optic gyroscope; Fiber optics; Information management; Light; Neural networks; overfitting; prediction model; Semiconductors; Sensors; Temperature effects; thermal errors; Thermal properties; Velocity; overfitting; thermal errors; biased regression; prediction model; fiber optic gyroscope
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23239450

For a fiber optic gyroscope, thermal deformation of the fiber coil can introduce additional thermal-induced phase errors, commonly referred to as thermal errors. Implementing effective thermal error compensation techniques is crucial to addressing this issue. These techniques operate based on the real-time sensing of thermal errors and subsequent correction within the output signal. Given the challenge of directly isolating thermal errors from the gyroscope’s output signal, predicting thermal errors based on temperature becomes necessary. To establish a mathematical model correlating the temperature and thermal errors, this study measured synchronized data of phase errors and angular velocity for the fiber coil under various temperature conditions, aiming to model it using data-driven methods. However, due to the difficulty of conducting tests and the limited number of data samples, direct engagement in data-driven modeling poses a risk of severe overfitting. To overcome this challenge, we propose a modeling algorithm that effectively integrates theoretical models with data, referred to as the TD-model in this paper. Initially, a theoretical analysis of the phase errors caused by thermal deformation of the fiber coil is performed. Subsequently, critical parameters, such as the thermal expansion coefficient, are determined, leading to the establishment of a theoretical model. Finally, the theoretical analysis model is incorporated as a regularization term and combined with the test data to jointly participate in the regression of model coefficients. Through experimental comparative analysis, it is shown that, relative to ordinary regression models, the TD-model effectively mitigates overfitting caused by the limited number of samples, resulting in a substantial 58% improvement in predictive accuracy.