Abstract
The impact of temperature on accelerometer will directly influence the precision of the inertial navigation system (INS). To eliminate the measurement error of accelerometer, this paper proposes a proximal support vector regression (PSVR) algorithm for generating a linear or nonlinear regression which requires the solution to single system of linear equations. PSVR is used to identify the static temperature model of the accelerometer. In order to improve the identifying performance, the kernel parameters and penalty factors of PSVR are optimized by the canonical particle swarm optimization (CPSO). The experiments under different temperature conditions were conducted. The experimental results show that the proposed PSVR can correctly identify the static temperature model of quartz flexure accelerometer and is more efficient than those of the standard SVR and least square algorithm.
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This work was supported by the National Key Basic Research and Development Program (No. 61388).
Xiangtao YU was born in Shandong, China, in 1979. He received his Ph.D. degree in Control Theory and Control Engineering from Beijing Institute of Technology in 2007. He is a senior engineer in ASIT Co., Ltd. His research interests include control system molding and optimization, inertial technology, and fault diagnosis.
Lan ZHANG was born in 1972. She is the director of the 5th research office in ASIT Co., Ltd. Her research interest is inertial technology.
Linrui GUO was born in 1979. He received his Ph.D. degree from Tsinghua University in 2006. He is the deputy director of the 5th research office in ASIT Co., Ltd. His research interest is inertial technology.
Feng ZHOU was born in 1978. He is the deputy director of the 5th research office in ASIT Co., Ltd. His research interest is inertial technology.
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Yu, X., Zhang, L., Guo, L. et al. Identification for temperature model of accelerometer based on proximal SVR and particle swarm optimization algorithms. J. Control Theory Appl. 10, 349–353 (2012). https://doi.org/10.1007/s11768-012-0076-0
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DOI: https://doi.org/10.1007/s11768-012-0076-0