Multi-parameter Identification of PMSM Based on IGWO Algorithm

To solve the multi-parameter identification problem of permanent magnet synchronous motor (PMSM), an improved Grey Wolf Optimizer (IGWO) algorithm is proposed. Firstly, the nonlinear convergence strategy is introduced to overcome the shortcoming of the linear convergence of Grey Wolf Optimizer (GWO) algorithm. Secondly, the coefficient weights of wolf α, β and δ are adjusted in the location update to highlight the impact of wolf α. Finally, dimension-learning-based hunting (DLH) optimization strategy is added to improve the diversity of the optimization process. The simulation results show that the proposed IGWO has higher precision and faster convergence speed for multi-parameter identification of PMSM.