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Materials 2017, 10(10), 1162; https://doi.org/10.3390/ma10101162

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

1
State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Received: 29 August 2017 / Revised: 25 September 2017 / Accepted: 3 October 2017 / Published: 10 October 2017
(This article belongs to the Section Structure Analysis and Characterization)
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Abstract

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. View Full-Text
Keywords: input forces estimation; nonlinear algorithm; square-root cubature Kalman filter; nonlinear estimator input forces estimation; nonlinear algorithm; square-root cubature Kalman filter; nonlinear estimator
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Song, X.; Zhang, Y.; Liang, D. Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter. Materials 2017, 10, 1162.

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