Inverter Nonlinearity Compensation in the Presence of Current Measurement Errors and Switching Device Parameter Uncertainties.

This paper proposes a compensation strategy for the unwanted disturbance voltage due to inverter nonlinearity. We employ an emerging learning technique called support vector regression (SVR). SVR constructs a motor dynamic voltage model by a linear combination of the current samples in real time. The model exhibits fast observer dynamics and robustness to observation noise. Then the disturbance voltage is estimated by subtracting the constructed voltage model from the current controller output. The proposed method compensates for all of the inverter nonlinearity factors at the same time. All the processes in estimating distortions are independent of the dead time and power device parameters. From the analysis of the effect on current measurement errors, we confirmed that the sampling error had little negative impact on the proposed estimation method. Experiments demonstrate the superiority of the proposed method in suppressing voltage distortions caused by inverter nonlinearity. [ABSTRACT FROM AUTHOR]