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This paper extends the ensemble Kalman filter (EnKF) for inverse problems toidentify trending model coefficients. This is done by repeatedly inflating theensemble while maintaining the mean of the particles. As a benchmark serves aclassic EnKF and a recursive least squares (RLS) on the example of identifyinga 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 whiteGaussian noise. The results demonstrate the feasibility of the approach fordynamic identification while still achieving good accuracy in the static case.Further, the inflated EnKF shows a remarkably insensitivity on the starting setbut a less smooth convergence compared to the classic EnKF.

Identifying trending coefficients with an ensemble Kalman filter