A new artificial neural network controller for an interior permanent magnet motor drive

This paper presents a novel dynamic artificial neural network (ANN) controller for accurate speed control of an interior permanent magnet synchronous motor (IPMSM) under system uncertainties. A field oriented IPMSM model is used to decouple the flux and torque components of the motor dynamics. The initial estimation of coefficients of the proposed ANN speed controller is obtained by an off-line training method. On-line training has been carried out to update the ANN under continuous mode of operation. Dynamic back-propagation (BP) with Levenburg-Marquardt algorithm (LM) is utilized for online training purpose. The simulation and experimental results reveal that the control architecture adapts and generalizes its learning to a wide range of operating conditions and provides promising results under parameter variations and load changes.