Sensorless vector control of permanent magnet synchronous motor based on adaptive extended Kalman filter
In the case of gross error interference or noise statistical deviation,the accuracy of extended Kalman filter(EKF)in speed estimation and rotor position prediction of permanent magnet synchronous motor(PMSM)is decreased.An adaptive Kalman filter algorithm based on innovation sequence was pro-posed.First,the gross error interference was added to the system observation equation,and its influence on the observation accuracy was analyzed.Secondly,in order to strengthen immunity of above system,the weighting coefficient was set in the innovation covariance calculation to complete the calculation of in-novation covariance matrix by adjusting the weight of the innovation covariance matrix at the adjacent time,and import the value into the Kalman gain calculation.Finally,the AEKF mathematical model was established based on the above steps,and the observation performance of AEKF and EKF was compared under the condition of deviation of gross interference and noise statistics.Simulation and experimental re-sults show that AEKF algorithm has stronger robustness and higher prediction accuracy for PMSM speed under the interference of gross error or noise statistics.
permanent magnet synchronous motorrotor position and speed estimationsensorlessadap-tive extended Kalman filtervector control
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