Unscented Kalman filtering for nonlinear structural dynamics

作     者：Mariani, Stefano Ghisi, Aldo 

作者机构：Politecn Milan Dipartimento Ingn Strutturale I-20133 Milan Italy 

出 版 物：《NONLINEAR DYNAMICS》 (非线性动力学)

年 卷 期：2007年第49卷第1-2期

页      面：131-150页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Kalman filter nonlinear structural dynamics parameter identification statistical linearization 

摘      要：Joint estimation of unknown model parameters and unobserved state components for stochastic, nonlinear dynamic systems is customarily pursued via the extended Kalman filter (EKF). However, in the presence of severe nonlinearities in the equations governing system evolution, the EKF can become unstable and accuracy of the estimates gets poor. To improve the results, in this paper we account for recent developments in the field of statistical linearization and propose an unscented Kalman filtering procedure. In the case of softening single degree-of-freedom structural systems, we show that the performance of the unscented Kalman filter (UKF), in terms of state tracking and model calibration, is significantly superior to that of the EKF.