Unscented Kalman filtering for nonlinear structural dynamics.

Stefano Mariani; Aldo Ghisi

Abstract  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.

CONTROL theory (Engineering); ESTIMATION theory; KALMAN filtering; STOCHASTIC processes

Nonlinear Dynamics, 2007, Vol 49, Issue 1/2, p131

0924-090X

Academic Journal

10.1007/s11071-006-9118-9