Radial Basis Function Neural Network-Based Inverter Nonlinearity Compensation for PMSM Sensorless Drives

The inverter nonlinearity induces current harmonics and mismatches between permanent magnet synchronous machine reference voltages and terminal voltages, which will degrade the sensorless drive system performance, especially at the low-speed range. In this paper, a radial basis function neural network (RBFNN)-based voltage compensator is proposed to suppress the current ripple. Without the requirement of any additional hardware or complex signal analysis procedure and processing algorithm, the RBFNN is self-tuned to directly generate the compensation voltage with the objective to minimize the current tracking error, so as to improve the active flux modeling accuracy and reduce position and speed estimation fluctuation.