Offline parameter identification of PMSMs based on physically feasible magnetic model with magnetic constraints

Accurate parameter identification of permanent magnet synchronous machines (PMSMs), including stator resistance, inductances, and permanent magnet flux linkage, is crucial for advanced controller design in robotic systems. Conventional identification methods, utilizing polynomial-based magnetic models to capture magnetic saturation and cross-coupling effects, suffer from degraded inductance identification accuracy under nonideal factors such as inverter nonlinearity and core loss effects. To address this issue, this paper proposes an offline parameter identification method for PMSMs based on a physically feasible magnetic model. This model explicitly incorporates the magnetic constraints of PMSMs, effectively preventing overfitting and enhancing inductance identification accuracy. A sensitivity analysis via an ablation study shows that the symmetric and mutual inductance constraints mainly suppress overfitting, while the magnetic saturation constraint mainly reduces model complexity. Based on this model, a high-frequency (HF) current injection method is developed for inductance identification. Crucially, this method eliminates the need for numerical differentiation of the magnetic model, thereby substantially improving the robustness of inductance identification against nonideal factors. Additionally, stator resistance and inverter nonlinearity are estimated simultaneously by solving a constrained nonlinear least-squares problem. Finally, the permanent magnet flux linkage is identified based on the steady-state voltage equation when rotor rotation is permitted. The effectiveness of the proposed physically feasible magnetic model and offline parameter identification method is experimentally validated on a 0.21-kW surface-mounted PMSM. Compared with conventional polynomial-based methods, it avoids overfitting and achieves higher inductance identification accuracy with lower cross-validation errors. Furthermore, the identified parameters improve the accuracy of MTPA control and remain robust under different thermal conditions.