Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter
AbstractThis 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
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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.
Song X, Zhang Y, Liang D. Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter. Materials. 2017; 10(10):1162.Chicago/Turabian Style
Song, Xuegang; Zhang, Yuexin; Liang, Dakai. 2017. "Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter." Materials 10, no. 10: 1162.
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