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

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.

[1]  Stephen Del Marco,et al.  Improved transient signal detection using a wavepacket-based detector with an extended translation-invariant wavelet transform , 1997, IEEE Trans. Signal Process..

[2]  W. Tian,et al.  An AGO–SVM drift modelling method for a dynamically tuned gyroscope , 2005 .

[3]  Adrian S. Lewis,et al.  Image compression using the 2-D wavelet transform , 1992, IEEE Trans. Image Process..

[4]  Jun Ni,et al.  The improvement of thermal error modeling and compensation on machine tools by CMAC neural network , 1996 .

[5]  Yanhua Zhang,et al.  A novel intelligent strategy for improving measurement precision of FOG , 2000, IEEE Trans. Instrum. Meas..

[6]  Péter András,et al.  Orthogonal RBF Neural Network Approximation , 1999, Neural Processing Letters.

[7]  Weifeng Tian,et al.  Temperature drift modelling of fibre optic gyroscopes based on a grey radial basis function neural network , 2004 .

[8]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[9]  Hiranmay Saha,et al.  Modeling, simulation and temperature compensation of porous polysilicon capacitive humidity sensor using ANN technique , 2005, Microelectron. Reliab..

[10]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[11]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[12]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[13]  Julien Weiss,et al.  Effect of bridge imbalance on the estimation of sensitivity coefficients for constant-temperature hot-wire anemometers , 2003 .

[14]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[15]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  P. S. Goel,et al.  Autonomous fault-tolerant attitude reference system using DTGs in symmetrically skewed configuration , 1989 .

[17]  Jang Gyu Lee,et al.  Digital rebalance loop design for a dynamically tuned gyroscope using H2 methodology , 2002 .

[18]  Zhu Rong.  Identification of Temperature Drift for FOG Using RBF Neural Networks , 2000 .

[19]  Haisheng Li,et al.  Application of support vector machine method in prediction of Kappa number of kraft pulping process , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).