Laser Gyro Temperature Compensation Using Modified RBFNN

Sensors

Jicheng Ding¹  Jian Zhang²  Weiquan Huang² 

[1] College of Automation, Harbin Engineering University, Harbin 150001, China;

关键词: laser gyro;    temperature compensation;    radial basis function neural network;    Kohonen network;    orthogonal least squares;   

DOI  :  10.3390/s141018711

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.