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