Field-oriented control of induction motors using neural-network decouplers

This paper presents a novel approach to the field-oriented control (FOG) of induction motor drives. It discusses the introduction of artificial neural networks (ANN's) for decoupling control of induction motors using FOC principles. Two ANN's are presented for direct and indirect FOC applications. The first performs an estimation of the stator flux for direct field orientation, and the second is trained ten map the nonlinear behavior of a rotor-flux decoupling controller. A decoupling controller and flux estimator were implemented upon these ANN's using the MATLAB/SIMULINK neural-network toolbox. The data for training are obtained from a computer simulation of the system and experimental measurements. The methodology used to train the networks with the backpropagation learning process is presented. Simulation results reveal some very interesting features and show that the networks have good potential for use as an alternative to the conventional field-oriented decoupling control of induction motors.