Temperature drift modelling and compensation for a dynamically tuned gyroscope by combining WT and SVM method

摘要

Temperature drift is the main source of errors affecting the precision and performance of a dynamically tuned gyroscope (DTG). In this paper, the support vector machine (SVM), a novel learning machine based on statistical learning theory (SLT), is described and applied in the temperature drift modelling and compensation to reduce the influence of temperature variation on the output of the DTG and to enhance its precision. To improve the modelling and compensation capability, wavelet transform (WT) is introduced into the SVM model to eliminate any impactive noises. The real temperature drift data set from the long-term measurement system of a certain DTG is employed to validate the effectiveness of the proposed combination strategy. Moreover, the traditional neural network (NN) approach is also investigated as a comparison with the SVM based method. The modelling and compensation results indicate that the proposed WT-SVM model outperforms the NN and single SVM models, and is feasible and effective in temperature drift modelling and compensation of the DTG.