Online Parameter Identification for Sensorless PMSM Drives with Inverter Nonlinearity Compensation

Online parameter identification is important for sensorless permanent magnet synchronous motor (PMSM) drives because motor parameter variation can reduce the accuracy of the controller and observer. However, in the background of sensorless control, the accuracy of online parameter identification is significantly affected by rotor position estimation errors and inverter nonlinearity. To address these problems, this paper proposes a high-frequency d-axis voltage injection-based online parameter identification method with inverter nonlinearity compensation. The proposed online identification method can identify the stator resistance and d-axis inductance independently. It not only overcomes the rank-deficiency problem in conventional voltage-equation-based identification, but also shows through theoretical analysis that the identification results are insensitive to rotor position estimation errors. To improve the identification accuracy, the influence and importance of inverter nonlinearity on parameter identification are analyzed, and a compensation method based on zero-sequence voltage characteristics and a feedforward neural network is developed. The identified voltage error is compensated through equivalent dead-time correction. Simulation and experimental results verify the advantages of the proposed method under different operating conditions.