Identification for temperature model of accelerometer based on proximal SVR and particle swarm optimization algorithms  

作　　者：Xiangtao YU Lan ZHANG Linrui GUO Feng ZHOU 

机构地区：[1]Aerospace Science and Industry Inertial Technology Co.,Ltd.,Beijing 100074,China

出　　处：《控制理论与应用（英文版）》2012年第3期349-353,共5页

基　　金：supported by the National Key Basic Research and Development Program(No.61388)

摘　　要：The impact of temperature on accelerometer will directly influence the precision of the inertial naviga- tion 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.The impact of temperature on accelerometer will directly influence the precision of the inertial naviga- tion 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.

关 键 词：Proximal support vector regression Particle swarm optimization System identification Quartz flexureaccelerometer Inertial navigation system 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TH824.4[自动化与计算机技术—计算机科学与技术]