ADALINE-Network-Based PLL for Position Sensorless Interior Permanent Magnet Synchronous Motor Drives

Abstract

To diminish the harmonic ripple in the rotor position estimation error for sensorless interior permanent magnet synchronous motor (IPMSM) drives, an adaptive linear neural (ADALINE)-network-based filter with a quadrature phase-locked loop (PLL) position observer is proposed. A fundamental model-based sliding-mode observer is used to obtain back electromotive force information. The harmonic content in the position estimation error caused by inverter nonlinearities and flux spatial harmonics is analyzed. According to the harmonic characteristics of the estimated rotor position, the ADALINE-network-based filter is adopted to track and compensate the selective harmonic ripple in the rotor position estimation error through continuously updating the filter weights online. Whereby, the position estimation accuracy can be improved and the position sensorless control performance can be promoted. The proposed method was verified for a 2.2-kW position sensorless IPMSM drive. Experimental results confirm the effectiveness of the method in suppressing the harmonic ripple in the rotor position estimation error.