Dynamic angular velocity modeling and error compensation of one-fiber fiber optic gyroscope (OFFOG) in the whole temperature range
Dynamic angular velocity modeling and error compensation of one-fiber fiber optic gyroscope (OFFOG) in the whole temperature range
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DOI：
10.1088/0957-0233/23/2/025101
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发表时间：
2012-01
影响因子：
2.4
通讯作者：
Yanshun Zhang;Yuanyuan Wang;Tao Yang;Rui Yin;Jiancheng Fang
中科院分区：
文献类型：
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作者：
Yanshun Zhang;Yuanyuan Wang;Tao Yang;Rui Yin;Jiancheng Fang
本文提出了基于径向基函数（RBF）神经网络的VG095M在整个温度范围内的动态角速度建模和误差补偿。以陀螺仪输出电压和环境温度为输入，角速度为输出，建立RBF神经网络模型。该模型通过实验数据进行训练和验证。模型的拟合误差为4.3818×10−6 deg s−1，表明该模型具有较高的精度。除建模数据外的实验数据均采用该模型进行处理。结果表明，补偿后角速度的最大、最小和均方误差分别减小到4.6%、4.3%和4.7%。
Dynamic angular velocity modeling and error compensation of VG095M in the whole temperature range, based on a radial basis function (RBF) neural network, is presented in this paper. With gyro output voltage and environmental temperature as the input and angular velocity as the output, an RBF neural network model is established. The model is trained and validated by the experiment data. The fitting error of the model is 4.3818 × 10−6 deg s−1, which shows that the model has high precision. The experiment data except the data used for modeling were processed with this model. The results show that the maximum, minimum and mean square error of the angular velocity were reduced to 4.6%, 4.3% and 4.7% respectively after compensation.