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A new algorithm for speed observer based on Model Reference Adaptive System (MRAS) is proposed for high performance induction motor drive. It uses stator current error based MRAS speed observer. The reference model of the stator current error based MRAS is the measured stator current components and the adaptive model is neuro-fuzzy based stator current observer. The adaptive model also needs the use...
This paper presents Adaptive Neuro-Fuzzy Inference System (ANFIS) based intelligent control of vector controlled induction motor drive. The proposed intelligent control scheme consists of sensorless adaptive neuro-fuzzy speed controller with speed estimation based on adaptive neuro-fuzzy inverse model. The proposed neuro-fuzzy speed controller incorporates fuzzy logic algorithm with a five-layer artificial...
In this paper, a high performance speed control approach using artificial neural networks (ANNs) and fuzzy logic for the field oriented induction motor (IM) is proposed. This control method is developed using model reference adaptive control (MRAC) to improve the performance of the IM speed. By using an adaptive neural network controller (ANNC) in the MRAC method, the speed of an IM can be controlled...
In this paper, because the induction machines are described as the plants of highly nonlinear and parameters time-varying, in order to obtain a very well control performances that a conventional model reference adaptive inverse control (MRAIC) can not be gotten, a fuzzy neural network-based model reference adaptive inverse control strategy for induction motors is presented based on the rotor field...
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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