Neurocontroller for induction motors

In this paper, the artificial neural network (ANN) is used to identify and control an induction machine by performing as a field-oriented control (FOC). The control system designed in this work is called the neurocontroller, and it is trained to reflect the nonlinear behavior of an indirect vector controller and of the PI controller used in the control system. Once the neurocontroller captures the nonlinear dynamics of the induction motor control, it can replace the conventional field-oriented controller (FOC) and the PI controller with a similar dynamic performance. The data used for the training of ANNs are obtained from computer simulation of the field oriented control (FOC). The methodology used to train the ANNs is the backpropagation algorithm (BP). Simulation results reveal some very interesting features of the neurocontroller and show that the network has good potential for use as an alternative to the conventional FOC decoupling control of induction motors, with the further advantage of being insensitive to parametric variations