Identifying trending model coefficients with an ensemble Kalman filter – a demonstration on a force model for milling
Max Schwenzer, Giuseppe Visconti, Muzaffer Ay, Thomas Bergs, Michaël Herty, Dirk Abel
Abstract
This paper extends the ensemble Kalman filter (EnKF) for inverse problems to identify trending model coefficients. This is done by repeatedly inflating the ensemble while maintaining the mean of the particles. As a benchmark serves a classic EnKF and a recursive least squares (RLS). As an example serves the identification of a force model in milling, which changes due to the progression of tool wear. For a proper comparison, the true values are simulated and augmented with white Gaussian noise. The results demonstrate the feasibility of the approach for dynamic identification while still achieving good accuracy in the static case. Further, the inflated EnKF shows a remarkably insensitivity on the starting set but a less smooth convergence compared to the classic EnKF.