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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. To improve low speed operation, the drive...
This paper presents an dSPACE implementation of three-phase squirrel-cage induction motor control using direct torque control (DTFC) technique with the help of intelligent techniques. A fuzzy PI controller is used in the speed control loop. The classical lookup table is replaced by a neural network selector providing switching state to the inverter. The rotor speed is estimated by a simple open loop...
This paper discusses the application of neural networks to control induction machines using direct torque control (DTC). The authors proposes to replace the conventional selector switches statements of the voltage inverter by a selector based on artificial neural networks (ANN), which is able to manage in the same way the switches states. In order to improve the low speed performance a closed loop...
This paper presents an on-line estimation for the stator resistances of the induction motor in the direct torque controlled drive, using artificial neural networks. The variation of stator resistance due to changes in temperature or frequency degrades the performance of such control strategy. In order to solve this issue, a backpropagation algorithm is used for training of the neural network. The...
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...
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