Online adaptation of two-parameter inverter model in sensorless motor drives

This article designs parameter adaptation algorithms for online simultaneous identification of a two-parameter sigmoid inverter model for compensating inverter nonlinearity to reduce the voltage error in flux estimation for a position sensorless motor drive. The inverter model has two parameters, $a_2$ and $a_3$, where $a_2$ is “plateau voltage” and $a_3$ is a shape parameter that mainly accounts for the stray capacitor effect. Parameter $a_3$ is identified by the $(6k\pm 1)$th order harmonics in measured current. Parameter $a_2$ is identified by the amplitude mismatch of the estimated active flux. It is found that the classic linear flux estimator, i.e., the hybrid of voltage model and current model, cannot be used for $a_2$ identification. This article proposes to use a saturation function based nonlinear flux estimator to build an effective indicator for $a_2$ error. The coupled identifiability of the two parameters is revealed and analyzed, which was not seen in literature. The concept of the low current region where the two-way coupling between $a_2$ and $a_3$ occurs is established. In theory, it is suggested to stop the inverter identification in the low current region. However, the experimental results in which dc bus voltage variation and load change are imposed have shown the effectiveness of the proposed online inverter identification and compensation method, even in low current region.