摘要
Biasofring-laser-gyroscope(RLG)changeswithtemperatureinanonlinearway.ThisisanimportantrestrainingfactorforimprovingtheaccuracyofRLG.Consideringthelimitationsofleast-squaresregressionandneuralnetwork,weproposeanewmethodoftemperaturecompensationofRLGbiasbuildingfunctionregressionmodelusingleast-squaressupportvectormachine(LS-SVM).StaticanddynamictemperatureexperimentsofRLGbiasarecarriedouttovalidatetheeffectivenessoftheproposedmethod.Moreover,thetraditionalleast-squaresregressionmethodiscomparedwiththeLS-SVM-basedmethod.TheresultsshowthemaximumerrorofRLGbiasdropsbyalmosttwoordersofmagnitudeafterstatictemperaturecompensation,whilebiasstabilityofRLGimprovesbyoneorderofmagnitudeafterdynamictemperaturecompensation.Thus,theproposedmethodreducestheinfluenceoftemperaturevariationonthebiasoftheRLGeffectivelyandimprovestheaccuracyofthegyroscopeconsiderably.
出版日期
2011年05月15日（中国期刊网平台首次上网日期，不代表论文的发表时间）
