General Formulation of Kalman-Filter-Based Online Parameter Identification Methods for VSI-Fed PMSM
Xinyue Li, Ralph Kennel
Abstract
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 20 kHz is demonstrated via experiments.