Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter

This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forc...

Full description

Saved in:
Bibliographic Details
Published in:Materials 2017-10, Vol.10 (10), p.1162
Main Authors: Song, Xuegang, Zhang, Yuexin, Liang, Dakai
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Covariance
Equations of state
Innovations
Kalman filters
Least squares method
Nonlinear systems
Runge-Kutta method
White noise
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equations; a square-root cubature Kalman filter (SRCKF) was employed to suppress white noise; the residual innovation sequences, a priori state estimate, gain matrix, and innovation covariance generated by SRCKF were employed to estimate the magnitude and location of input forces by using a nonlinear estimator. The nonlinear estimator was based on the least squares method. Numerical simulations of a large deflection beam and an experiment of a linear beam constrained by a nonlinear spring were employed. The results demonstrated accuracy of the nonlinear algorithm.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma10101162