Abstract
To overcome the effect of temperature on laser gyro zero bias and to stabilize the laser gyro output, this study proposes a modified radial basis function neural network (RBFNN) based on a Kohonen network and an orthogonal least squares (OLS) algorithm. The modified method, which combines the pattern classification capability of the Kohonen network and the optimal choice capacity of OLS, avoids the random selection of RBFNN centers and improves the compensation accuracy of the RBFNN. It can quickly and accurately identify the effect of temperature on laser gyro zero bias. A number of comparable identification and compensation tests on a variety of temperature-changing situations are completed using the multiple linear regression (MLR), RBFNN and modified RBFNN methods. The test results based on several sets of gyro output in constant and changing temperature conditions demonstrate that the proposed method is able to overcome the effect of randomly selected RBFNN centers. The running time of the method is about 60 s shorter than that of traditional RBFNN under the same test conditions, which suggests that the calculations are reduced. Meanwhile, the compensated gyro output accuracy using the modified method is about 7.0 × 10−4 °/h; comparatively, the traditional RBFNN is about 9.0 × 10−4 °/h and the MLR is about 1.4 × 10−3 °/h.
Highlights
The stability of the output of a laser gyro, which is a high-precision optical angular rate sensor, directly affects the accuracy of the laser inertial navigation system (LINS) [1]
The zero bias of laser gyro is closely related to the temperature of its operating environment and to temperature change rate; addressing the negative effect of temperature and temperature change rate on the zero bias of laser gyro improves the accuracy of the LINS [5,6]
A temperature compensation model based on multiple linear regression (MLR) [9] has been used to improve the traditional compensation effect
Summary
The stability of the output of a laser gyro, which is a high-precision optical angular rate sensor, directly affects the accuracy of the laser inertial navigation system (LINS) [1]. Temperature changes affect the physical properties, geometry and gas flow field of the laser gyro Such changes can activate the anomalous dispersion effect of a medium, resulting in scale factor error and the zero bias of a laser gyro [2]. Numerous experiments have demonstrated that the laser gyro scale factor changes slightly under different temperature conditions, but the zero bias is known to be the most susceptible to this change [3,4]. A back propagation neural network (BPNN) has improved identification accuracy significantly by accurately fitting the temperature characteristics of laser gyros [11,12].
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