583 RWTH Publication No: 811910        2020       
TITLE Identifying trending coefficients with an ensemble Kalman filter
AUTHORS Max Schwenzer, Giuseppe Visconti, Muzaffer Ay, Thomas Bergs, Michael 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) on the example of identifying 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.
KEYWORDS Ensemble Kalman filter, Recursive least squares, Manufacturing, Milling, Parameter identification, Identification, Time-invariant identification
DOI 10.1016/j.ifacol.2020.12.1490
PUBLICATION IFAC-PapersOnLine, Volume 53, Issue 2, 2020, Pages 2292-2298