Neural controller design for speed control of an indirect field oriented induction machine drive

Artificial neural networks (ANN) have the capacity to learn the characteristics of a nonlinear system through nonlinear mappings. They are then potential candidates for highly nonlinear dynamical processes control. In this paper, a neural network controller design for speed adjustment of an indirect field oriented induction machine drive is considered. An original PI based controller is first proposed. Its simulated input-output nonlinear relationship is then learned off-line using a feed-forward linear network with one hidden layer. The simulation of the neural network controlled system shows promising results. The motor reaches the reference speed rapidly and without overshoot, step commands are tracked with almost zero steady state error and no overshoot, load disturbances are rapidly rejected and variations of some of the motor parameters are fairly well dealt with