【目的】针对内置式永磁同步电机(IPMSM)在无位置传感器控制过程中,易受磁路饱和因素干扰,从而引发dq轴电感参数变化及模型失配导致控制性能下降的问题,本文基于扩展卡尔曼滤波(EKF)在线辨识方法对dq轴电感进行实时估计。【方法】依据非线性系统的最优递推估计原理设计了EKF算法,通过状态预测与误差协方差矩阵的实时更新处理系统及测量噪声,并将辨识所得电感参数前馈至无位置传感器观测算法中,实现电机模型的动态更新与闭环反馈校正,提升系统对参数变化及复杂工况的鲁棒性。【结果】试验结果表明,引入EKF在线参数辨识后,在低速带载工况下仍能维持良好动态及稳态性能;在高速域下突加负载时,转子位置观测精度提升1.5°。【结论】通过与普通最小二乘法对比,证实EKF参数辨识效果更优,有效抑制磁饱和及交叉饱和效应对位置估计的影响。

[Objective] To address the issue that the sensorless control process of interior permanent magnet synchronous motor (IPMSM) is susceptible to magnetic circuit saturation, which induces variations in dq-axis inductance parameters and model mismatch thereby degrading control performance, this paper employs an extended Kalman filter (EKF)-based online identification method to estimate the dq-axis inductances in real time. [Methods] The EKF algorithm was designed based on the optimal recursive estimation principle for nonlinear systems. System and measurement noises were processed through real-time updates of state prediction and the error covariance matrix. The identified inductance parameters were fed forward into the sensorless observer algorithm to achieve dynamic updating of the motor model and closed-loop feedback correction, thereby enhancing the system’s robustness against parameter variations and complex operating conditions. [Results] The experimental results indicated that with the introduction of EKF online parameter identification, favorable dynamic and steady-state performance was still maintained under low-speed loaded conditions; moreover, when a sudden load was applied in the high-speed region, the rotor position observation accuracy was enhanced by 1.5°. [Conclusion] Comparative analysis with the ordinary least squares method confirms that the EKF-based parameter identification demonstrates superior performance, effectively suppressing the impact of magnetic saturation and cross-saturation effects on position estimation.

国家自然科学基金(52477046)