This article proposes two Kalman-filter-based online identification schemes for permanent magnet synchronous machines (PMSMs), where the nonlinearity of a voltage-source inverter (VSI) is taken into account. One is formulated from an extended Kalman filter; the other uses a dual extended Kalman filter. They are generally formulated and can be applied to any identifiable electrical parameter combinations. The proposed schemes are further implemented on an industrial embedded control system. Their performance tests are conducted on a PMSM under static and dynamic conditions and compared with the extended Kalman filter without VSI nonlinearity compensation. The effectiveness of the proposed approaches is proved by the experimental results. Furthermore, a sensitivity analysis of the initial setup of parameter estimates has shown that the proposed estimators are robust against poor initial value choices. Real-time feasibility of the proposed estimators up to 20kHz\text{20}\;\text{kHz} is demonstrated via experiments.

中文翻译：

---

基于卡尔曼滤波器的 VSI-Fed PMSM 在线参数识别方法的通用公式


本文提出了两种基于卡尔曼滤波器的永磁同步电机（PMSM）在线识别方案，其中考虑了电压源逆变器（VSI）的非线性。一种是由扩展卡尔曼滤波器制定的；另一个使用双扩展卡尔曼滤波器。它们通常被制定并可应用于任何可识别的电气参数组合。所提出的方案进一步在工业嵌入式控制系统上实现。他们的性能测试是在静态和动态条件下对 PMSM 进行的，并与没有 VSI 非线性补偿的扩展卡尔曼滤波器进行了比较。实验结果证明了所提出方法的有效性。此外，对参数估计初始设置的敏感性分析表明，所提出的估计器对于不良初始值选择具有鲁棒性。通过实验证明了所提出的估计器在高达 20kHz\text{20}\;\text{kHz} 的实时可行性。