摘要
为了提高模型更新混合试验模型更新的准确性,在隐性卡尔曼滤波器(Unscented Kalman Filter,UKF)的基础上,提出约束UKF(Constrained UKF,CUKF)算法。算法采用样本点投影方法处理考虑界限约束的非线性模型参数在线识别问题。在预测步中同时修改违反约束条件的样本点位置和相应权重;在更新步中采用样本点更新方式,可同时实现约束对状态估计和协方差矩阵的影响。提出基于CUKF模型更新混合试验方法,在混合试验过程中,基于试验观测数据采用CUKF算法在线更新数值子结构模型。通过对两个自由度非线性结构混合试验数值模拟表明:与传统混合试验、基于UKF模型更新混合试验相比,基于CUKF模型更新混合试验结果具有更好的精度。
To improve accuracy of hybrid testing method based on model updating, a new Constrained Unscented Kalman Filter （CUKF） algorithm is proposed based on the Unscented Kalman Filter （UKF）, which adopts the sample points projecting method to deal with nonlinear model parameter online estimation with bounds constraints. In prediction step, positions and weights of sample points violating constraints are modified at the same time. In up- dating step, state estimation and errors covariance matrix calculation are combined with state constraints by sample points updating method. The hybrid testing method based on model updating with CUKF is presented. During hy- brid testing, the numerical substructure models are online updated with CUKF approach based on the measurement data from testing substructure. The effectiveness of the proposed test method is verified by a hybrid testing numeri- cal simulation for a nonlinear system with two degrees of freedom. The results show the new hybrid testing method has better accuracy compared with conventional hybrid testing and hybrid testing based on model updating with UKF.
出处
《地震工程与工程振动》
CSCD
北大核心
2013年第5期100-109,共10页
Earthquake Engineering and Engineering Dynamics
基金
国家自然科学基金项目(51161120360,91315301)
黑龙江省教育厅科技项目(12511485)
关键词
模型更新
实时混合试验
UKF
约束
恢复力模型
hybrid testing
model updating
unscented Kalman filter
constraint
restoring force model
