Temperature drift compensation of a FOG based on an HKSVM optimized by an improved hybrid BAS-GSA algorithm

In this paper, the optimal hybrid kernel support vector machine is employed to propose a compensation strategy intended for the temperature drift of a fiber optical gyroscope (FOG). First, the mode of the hybrid kernel with an interpolation and extrapolation capability is constructed, which consists of the radial basis function and the polynomial kernel function. Second, the combination model of the beetle antennae search algorithm and gravitational search algorithm that has both local and global search capability is proposed to optimize the structure-related parameters of a hybrid kernel support vector machine (HKSVM). Finally, the proposed approach is trained and tested using the experimental data of temperature drift at two different rates of temperature change (10°C/min and 5°C/min). In addition, the proposed method is validated against those conventional compensation algorithms. According to the research results, the compensation error (mean squared error) of the proposed approach is reduced by 92% compared to the traditional support vector machine based on the radial basis function.