Adaptive Offline Parameter Identification of PMSM With Nonlinear Compensation in Self-Commissioning

Accurate offline parameter identification of unknown permanent magnet synchronous machines (PMSMs) without tuned current controllers remains challenging due to nonlinearities and safety constraints. Therefore, this paper proposes a hybrid open/closed-loop identification strategy incorporating nonlinear compensation for automated parameter identification. An excitation-adaptive amplitude-frequency open-loop injection scheme is proposed to safely excite unknown PMSMs while eliminating phase-measurement errors in identification, thereby enabling efficient and high-precision open-loop parameter identification. Within a single closed-loop ramp current injection procedure, simultaneous stator resistance identification and inverter nonlinearity compensation are achieved, enhancing the speed and accuracy of identification. Furthermore, skin effect compensation is implemented via analysis of mid- and high-frequency current responses to improve inductance identification precision. The framework uniquely integrates compensation for critical nonlinearities while eliminating manual intervention. Validation on a 750W PMSM confirms enhanced accuracy, operational efficiency and reliability.