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

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.

Tacholess estimation of time-varying dynamic coefficients of journal bearing based on the square-root cubature Kalman filter

“…Similar to the descriptions in [32], the dynamic estimation model of the journal bearing-rotor system is established by adding a recurrence process of unknown vector βk:…”