Abstract: Permanent magnet synchronous motor in the multi-parameter identification will appear parameter coupling leads to identification model under-rank, the utilization rate of data is low, and identification parameter imprecision problem. In this paper, we propose a parameter identification of permanent magnet synchronous motor based on improved recursive least squares algorithm. Firstly, a mathematical model of permanent magnet synchronous motor is established on two synchronous rotating orthogonal coordinate systems, and secondly, the stepwise parameter identification is realized by combining the recursive least squares algorithm and the extended Kalman filtering algorithm, which avoids the parameter coupling resulting in the under-ranking of the identification model, and at the same time, the two algorithms introduce the multi innovation theory in the distribution of the identification, so as to increase the utilization of the data. Finally, the algorithm of this paper is compared with the original algorithm and the model reference adaptive dystem algorithm for parameter identification. The accuracy of identifying resistance, inductance and magnetic chain at constant load and speed is improved by 4.89%, 1.86% and 3.80% compared to the original algorithm, and 11.32%, 1.21% and 2.03% compared to the model reference adaptive algorithm. The fluctuation of this algorithm is smaller than that of the model reference adaptive algorithm when the load and rotational speed change suddenly, which indicates that this algorithm has higher accuracy and better stability in parameter identification.

Key words: permanent magnet synchronous motor, recursive least square algorithm, extended Kalman filter, stepwise parameter identification, multi innovation theory