MRAS-ANN based sensorless speed control for direct torque controlled induction motor drive

This paper presents speed sensorless direct torque control (DTC) of induction motor using Artificial intelligence (AI). The artificial neural network (ANN) MRAS-based speed estimation is used. The error between the reference model and the neural network based adaptive model is used to adjust the weights by on-line Back propagation (BP) training algorithm. The speed loop regulation is carried out by a fuzzy controller giving exceeding performance in comparison with a classic PI regulator. The performance of fuzzy speed controller and speed estimator are investigated with the help of Matlab/Simulink®. The estimated speed accuracy was achieved with high performance of the speed controller. The estimated speed error is less than 1% both in transient and steady-state operation. The fuzzy controller is robust to load torque perturbations and speed reference changes.