Multi-parameter identification of high-lift motor based on GWO-optimised EKF

Aiming at the problems of difficulty in determining suitable process and measurement noise matrices and low recognition accuracy of traditional methods when using Extended Kalman Filter (EKF) for parameter recognition of Permanent Magnet Synchronous Motors (PMSM), this paper proposes a method of PMSM parameter recognition using Grey Wolf Optimiser (GWO) algorithm to optimise the EKF. The paper firstly analyses the principle of EKF and establishes the EKF identification model, and then through the GWO adaptive optimisation of the noise covariance matrices of EKF, the adaptive function suitable for the EKF identification model is designed, so that the EKF can carry out the parameter identification in a better way. The proposed approach exhibits a reduction in the selection duration for the noise covariance matrices,QandR, and enhances both the recognition precision and efficiency compared with the conventional EKF algorithm.