基于STUKF的非线性结构系统时变参数识别

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (7) : 171-176.

论文

杜永峰1,2，张浩1，赵丽洁1，李万润1

作者信息 +

Time-varying parametric identification of nonlinear structural systems based on STUKF

DU Yongfeng1,2,ZHANG Hao1,ZHAO Lijie1,LI Wanrun1

Author information +

文章历史 +

摘要

针对非线性结构系统时变参数识别问题，传统无迹卡尔曼滤波(unscented Kalman filter, UKF)难以有效跟踪结构参数的变化。将强跟踪滤波原理引入无迹卡尔曼滤波，提出一种强跟踪无迹卡尔曼滤波(strong tracking unscented Kalman filter，STUKF)算法，以识别结构参数的变化。首先，在UKF量测更新后，依据输出残差计算渐消因子矩阵；其次，引入两个渐消因子矩阵实时调整状态预测协方差矩阵，使残差序列强行正交，快速修正结构参数估计值，使STUKF具有对结构参数变化的跟踪能力。此外，为节省计算时间，调整状态预测协方差矩阵后不再进行sigma点采样，保证了算法的高效性。数值分析结果表明，该算法能有效识别非线性结构系统的参数及其变化，并具有较强的抗噪性。

Abstract

Traditional unscented Kalman filter (UKF) cannot track the changed parameters of nonlinear structural systems.Based on the strong tracking filter principle,a strong tracking unscented Kalman filter (STUKF) method was put forward to identify the time-varying parameters of nonlinear structural systems.Firstly,the fading factor matrix was calculated with output residuals after the measurement update of UKF.Secondly,two fading factor matrices were introduced to adjust the predicted state covariance matrix in real time,the residual sequence was made to be orthogonal and the estimated values of structure parameters were updated rapidly,thus STUKF was made to be capable of tracking the changes of structure parameters.Furthermore,the computational efficiency was improved by taking no sigma points sampling after adjusting the predicted state covariance matrix.Numerical simulation results demonstrated that the proposed method can effecfively identify parameters and changes of nonlinear structural systems,and it has a stronger anti-noise capability.

导出引用

杜永峰1,2，张浩1，赵丽洁1，李万润1. 基于STUKF的非线性结构系统时变参数识别[J]. 振动与冲击, 2017, 36(7): 171-176

DU Yongfeng1,2,ZHANG Hao1,ZHAO Lijie1,LI Wanrun1. Time-varying parametric identification of nonlinear structural systems based on STUKF[J]. Journal of Vibration and Shock, 2017, 36(7): 171-176

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