VECTOR CONTROL OF A POSITION SENSORLESS SPMSM DRIVE WITH RNN BASED STATOR FLUX ESTIMATOR

Abstract

A position sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive based on single layer recurrent neural network is presented in this paper. The motor equations are written in rotor fixed d-q reference frame. A PID controller is used to process the speed error to generate the reference torque current keeping the magnetizing current fixed. The Recurrent Neural Network (RNN) estimator is used to estimate flux components along the stator fixed stationary axes (α-β). The flux angle and the reference current phasor angle are used in vector rotator to generate the reference phase currents. Hysteresis current controller block controls the switching of the three phase inverter to apply voltage to the motor stator. Simulation studies on different operating conditions indicate the acceptability of the drive system. The drive system only requires a speed transducer and is free from position sensor requirement. The proposed control scheme is robust under load torque disturbances and motor parameter variations. It is also simple and low cost to implement in a practical environment.