An UKF‐based nonlinear system identification method using interpolation models and backward integration

An UKF‐based nonlinear system identification method using interpolation models and backward... In this paper, a novel identification method for nonlinear systems is proposed. This method utilizes linear interpolation models to describe the nonlinear forces of the physical models, and the unscented Kalman filter (UKF) method is adopted for the task of nonlinear identification. With the help of a linear interpolation algorithm, the proposed method requires little prior knowledge of the form of the nonlinear stiffness. Therefore, this method takes advantage of both the independence of the linear interpolation points and the inherent mathematical properties of the UKF. The UKF method is also modified to better fit the needs of parameter identification. To further emphasize parameter identification, backward integration and observations of the previous states are used. Two numerical simulations of the nonlinear elastic stiffness and Bouc–Wen hysteresis are conducted to show the flexibility and efficiency of this method. In these 2 examples, the observation signals are generated by analytic models, and the identifications are conducted with a linear interpolation model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Structural Control and Health Monitoring Wiley

An UKF‐based nonlinear system identification method using interpolation models and backward integration

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1545-2255
eISSN
1545-2263
D.O.I.
10.1002/stc.2129
Publisher site
See Article on Publisher Site

Abstract

In this paper, a novel identification method for nonlinear systems is proposed. This method utilizes linear interpolation models to describe the nonlinear forces of the physical models, and the unscented Kalman filter (UKF) method is adopted for the task of nonlinear identification. With the help of a linear interpolation algorithm, the proposed method requires little prior knowledge of the form of the nonlinear stiffness. Therefore, this method takes advantage of both the independence of the linear interpolation points and the inherent mathematical properties of the UKF. The UKF method is also modified to better fit the needs of parameter identification. To further emphasize parameter identification, backward integration and observations of the previous states are used. Two numerical simulations of the nonlinear elastic stiffness and Bouc–Wen hysteresis are conducted to show the flexibility and efficiency of this method. In these 2 examples, the observation signals are generated by analytic models, and the identifications are conducted with a linear interpolation model.

Journal

Structural Control and Health MonitoringWiley

Published: Jan 1, 2018

Keywords: ; ; ; ; ;

References

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