基于多渐消因子强跟踪SRUKF的故障参数辨识

摘要

To solve the problem of nonlinear system fault parameter estimation,the multiple fading factors strong tracking square root unscented Kalman filter(MST⁃SRUKF)algorithm is proposed. The multiple fading factors are introduced into covari⁃ance matrix square root by means of MST⁃SRUKF. Then the fading factor computational formula suitable for square root unscent⁃ed Kalman filter(SRUKF)is deduced to adjust the gain matrix in SRUKF in real time to ensure filter accuracy when the model has big error or changes abruptly. The experiment result shows that,compared with SRUKF and strong tracking unscented Kal⁃man filter(STUKF),the MST⁃SRUKF has higher estimation accuracy of fault parameter.%为了解决非线性系统中故障参数估计问题，提出多重渐消因子强跟踪平方根无迹卡尔曼滤波（MST⁃SRUKF）算法。MST⁃SRUKF将多重渐消因子引入到协方差矩阵平方根中，推导适用于平方根无迹卡尔曼滤波（SRUKF）的渐消因子计算公式，从而实时调整SRUKF中的增益矩阵，保证其对模型存在较大误差或者突变情况下的滤波精度。实验结果表明，相比于SRUKF和强跟踪无迹卡尔曼滤波（STUKF），MST⁃SRUKF对故障参数具有更高的估计精度。