The use of an Extended Kalman Filter (EKF) as non- linear speed and position observer for Permanent Magnet Synchronous Motor (PMSM) drives is a mature research topic. Norwithstunding, the shift from research protowpe to a market-ready product still calls for a solution of some implementation pi@alls. The major and still unsolved topic is the choice of the EKF covariance matrices. This paper replaces the usual trial-and-error method with a straightforward matrices choice. These matrices, possibly combined with a novel self-tuning procedure, should put the EKF drive much closer to an of-the-shelf product. The proposed method is based on the complete normalisation of the EKF algorithm representation. Successful experimental results are included in the paper.
Extended Kalman Filter Tuning in Sensorless PMSM Drives
BOLOGNANI, SILVERIO;ZIGLIOTTO, MAURO;
2003
Abstract
The use of an Extended Kalman Filter (EKF) as non- linear speed and position observer for Permanent Magnet Synchronous Motor (PMSM) drives is a mature research topic. Norwithstunding, the shift from research protowpe to a market-ready product still calls for a solution of some implementation pi@alls. The major and still unsolved topic is the choice of the EKF covariance matrices. This paper replaces the usual trial-and-error method with a straightforward matrices choice. These matrices, possibly combined with a novel self-tuning procedure, should put the EKF drive much closer to an of-the-shelf product. The proposed method is based on the complete normalisation of the EKF algorithm representation. Successful experimental results are included in the paper.
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