Sensorless vector control of induction motors in fuel cell vehicle using a neuro-fuzzy speed controller and an online artificial neural network speed estimator

A sensorless speed control method for induction motors in a fuel cell vehicle is presented. An artificial neural network (ANN) estimates the speed, and a neuro-fuzzy controller (NFC) is used in the speed control loop to overcome the nonlinearity of the plant. A PI controller controls the motor flux and the NFC determines the required torque. The tuning of the NFC is simple and this is one of the advantages of NFCs compared with the conventional PI controllers. In addition, the nonlinear behavior of the NFC increases its robustness against variation of parameters in the plant. The speed estimation is done by a two-layer online neural network in the rotating coordinate fixed with rotor flux. The ANN estimator has a simple structure, and its parameters are adjusted online. The simulation and experimental results are given to prove the effectiveness of this approach.