A comparison of unscented and extended Kalman filtering for nonlinear system identification

Abdullah Al-Hussein, Achintya Haldar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

A nonlinear system identification-based structural health assessment procedure is presented in this paper. The procedure uses the unscented Kalman filter (UKF) concept. The weighted global iteration with an objective function is incorporated with the UKF algorithm to obtain stable, convergent, and optimal solution. An iterative least squares technique is also integrated with the UKF algorithm. The procedure is capable of assessing health of any type of structures, represented by finite elements. It can identify the structure using limited noise-contaminated dynamic responses, measured at a small part of large structural systems and without using input excitation information. In order to demonstrate its effectiveness, the proposed procedure is compared with the extended Kalman filter (EKF)-based procedure. For numerical verification, a two-dimensional five-story two-bay steel frame is considered. Defect-free and two defective states with small and severe defects are considered. The study shows that the proposed UKF-based procedure can assess structural health more accurately and efficiently than the EKF-based procedures for nonlinear system identification.

Original languageEnglish (US)
Title of host publication12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015
PublisherUniversity of British Columbia
ISBN (Electronic)9780888652454
StatePublished - 2015
Event12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 - Vancouver, Canada
Duration: Jul 12 2015Jul 15 2015

Other

Other12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012
CountryCanada
CityVancouver
Period7/12/157/15/15

Fingerprint

Extended Kalman Filtering
Nonlinear System Identification
Kalman filters
Nonlinear systems
Identification (control systems)
Kalman Filter
Health
Extended Kalman filters
Defects
Dynamic response
Numerical Verification
Steel
Dynamic Response
Least Squares
Objective function
Optimal Solution
Excitation
Finite Element
Iteration

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Statistics and Probability

Cite this

Al-Hussein, A., & Haldar, A. (2015). A comparison of unscented and extended Kalman filtering for nonlinear system identification. In 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015 University of British Columbia.

A comparison of unscented and extended Kalman filtering for nonlinear system identification. / Al-Hussein, Abdullah; Haldar, Achintya.

12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015. University of British Columbia, 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Al-Hussein, A & Haldar, A 2015, A comparison of unscented and extended Kalman filtering for nonlinear system identification. in 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015. University of British Columbia, 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012, Vancouver, Canada, 7/12/15.
Al-Hussein A, Haldar A. A comparison of unscented and extended Kalman filtering for nonlinear system identification. In 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015. University of British Columbia. 2015
Al-Hussein, Abdullah ; Haldar, Achintya. / A comparison of unscented and extended Kalman filtering for nonlinear system identification. 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015. University of British Columbia, 2015.
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