Parameter identification of permanent magnet synchronous motor based on an improved Aquila optimizer

Accurate parameter identification is essential for achieving high-performance control of Permanent Magnet Synchronous Motors (PMSMs). However, the strong nonlinearity, tight coupling, and complex operating conditions of PMSMs pose significant challenges to parameter identification, often resulting in slow convergence and insufficient accuracy in traditional methods. To address these challenges, this paper proposes an improved intelligent optimization approach. The method integrates the Aquila Optimizer (AO) with Logistic chaotic mapping and a Lévy flight strategy. By introducing chaotic mapping to enhance population diversity and employing an adaptive parameter mechanism, the switching capability between different search stages is improved. Furthermore, Lévy flight and mutation strategies are incorporated to enhance global exploration capabilities and mitigate the risk of entrapment in local optima. The proposed algorithm is first validated through performance testing and ablation studies. Then, a four-equation parameter identification model is established using two steady-state operating conditions in the d–q coordinate system of the PMSM, transforming the identification problem into an optimization problem. Finally, the effectiveness of the proposed method is verified through simulation and experimental studies. The results demonstrate that the improved algorithm achieves faster convergence and higher identification accuracy, with the experimental identification errors of all four parameters controlled within 1.4%. Compared with the selected comparison algorithms under the tested conditions, the proposed approach demonstrates better convergence performance, identification accuracy, and search stability, thereby enhancing the efficiency of multi-parameter identification in PMSMs.