Temperature compensation of laser gyro based on improved RBF neural network

Zhen, 史震 Shi; Shuai, 陈帅 Chen; Jian, 张健 Zhang; Lin, 赵琳 Zhao; Qian, 孙骞 Sun

doi:10.3788/ope.20142211.2975

Cited by 5 publications

(1 citation statement)

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“…There are several ways to compensate for the bias temperature of the laser gyro. These include Least Squares Method, Artificial Neural Network [7][8][9][10], Support Vector Machine [11][12][13]. Currently, compensation in engineering is usually fitted and compensated by Least Squares Method (LSM).…”

Section: Introductionmentioning

confidence: 99%

The Temperature Compensation Method for the Laser Gyro Based on the Relevance Vector Machine

Liang

Ren²,

Zhang³

et al. 2021

Lecture Notes in Electrical Engineering

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The laser gyro is an essential part of the inertial measurement unit. Its output precision directly affects the performance of the inertial measurement unit. Temperature is an important factor influencing the bias of the laser gyro. The bias drift of the gyro with temperature makes the output of the inertial measurement unit unstable. Temperature model and compensation model of the gyro and the inertial measurement unit are established in this paper. The bias compensation model based on the relevance vector machine is designed and optimized by the particle swarm optimization algorithm. The temperature compensation experiment is designed, and the bias is calculated using the calibration data after compensation. The experimental results show that the method proposed in this paper can effectively compensate for the bias of the laser gyro.

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Section: Introductionmentioning

confidence: 99%

The Temperature Compensation Method for the Laser Gyro Based on the Relevance Vector Machine

Liang

Ren²,

Zhang³

et al. 2021

Lecture Notes in Electrical Engineering

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MEMS Gyroscope Temperature Compensation Based on SSA-RBF Neural Network

Liu

Wang

Niu

2022

Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition

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System‐Level Temperature Compensation Method for the RLG‐IMU Based on HHO‐RVR

Liang

Tao²,

Wang³

et al. 2021

Journal of Sensors

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The ring laser gyro inertial measurement unit has many systematic error terms and influences each other. These error terms show a complex nonlinear drift that cannot be ignored when the temperature changes, which seriously affects the stability time and output accuracy of the system. In this paper, a system-level temperature modeling and compensation method is proposed based on the relevance vector regression method. First, all temperature-related parameters are modeled; meanwhile, the Harris hawks optimization algorithm is used to optimize each model parameter. Then, the system compensation is modeled to stabilize the system output to the desired temperature. Compared with the least square method, the fitting performance comparison and the system dynamic compensation experiment prove this method’s superiority. The root mean square error, the mean absolute error, the R -squared, and the variance of residual increased by an average of 35.27%, 39.29%, 2.29%, and 30.34%, respectively.

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Temperature compensation of laser gyro based on improved RBF neural network

Cited by 5 publications

References 0 publications

The Temperature Compensation Method for the Laser Gyro Based on the Relevance Vector Machine

The Temperature Compensation Method for the Laser Gyro Based on the Relevance Vector Machine

MEMS Gyroscope Temperature Compensation Based on SSA-RBF Neural Network

System‐Level Temperature Compensation Method for the RLG‐IMU Based on HHO‐RVR

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