Parameter Identification of Permanent Magnet Synchronous Motor Based on CGCRAO Algorithm

In response to the challenge of easily falling into local optima and slow identification speed in the parameter identification of permanent magnet synchronous motors (PMSMs), this paper proposes a CGCRAO algorithm based on chaotic initialization and a hybrid variation strategy. The algorithm uses Tent chaotic mapping for population initialization to improve population diversity. At the same time, by combining the Gaussian and Cauchy distribution characteristics and the three-stage operation idea, the optimal individual variation strategy is autonomously selected in real time to improve the RAO-1 algorithm. This paper validates the effectiveness of the algorithm improvement and the correctness of the three-stage operation idea using eight benchmark test functions. Furthermore, This paper conducted comparative experiments on parameter identification of five algorithms under different operating conditions through simulation and experiments. The results indicate that the proposed CGCRAO algorithm enables fast and accurate identification of PMSM parameters.