强跟踪平方根容积卡尔曼滤波和自回归模型融合的故障预测.PDF

31 8 Vol. 31 No. 5 2014 8 Control Theory Applications Aug. 2014 DOI: 10.7641/CTA.2014.30963 , , , (1. , 050003; 2. , 100085) : , (STSCKF)(AR). AR, STSCKF, . STSCKF , SCKF , STSCKF. (CSTR) , STSCKFSCKF(STUKF), . : ; ; ; (SCKF); : TP273 : A Fault prediction with combination of strong tracking square-root cubature Kalman filter and autoregressive model DU Zhan-long , LI Xiao-min , ZHENG Zong-gui , MAO Qiong (1. Department of UAV Engineering, Ordnance Engineering College, Shijiazhuang Hebei 050003, China; 2. Academe of Second Artillerist, Beijing 100085, China) Abstract: To deal with the problem of prognosis of unmeasured parameters in nonlinear systems, we propose a fault prediction algorithm which is a combination of the strong tracking square-root cubature Kalman filter (STSCKF) with suboptimal fading factor and the autoregressive (AR) model. Future time values of measurement variables are forecasted by using the AR model time series prediction method; and then, the predicted values are used as STSCKF measurement variables. Thus, the prognostic problem is transformed into a filter estimation issue. The fading factor is introduced into the square root of the STSCKF prediction error covariance for adjusting the gain matrix in the filter process. As a result, STSCKF eliminates the disadvantage of slow tracking or even unable tracking of fault parameters in conventional SCKF when the time-varying functions of fault parameters are unknown, improving the ca