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The design and implementation of an adaptive neural network based proportional-integral controller (NNPIC) were carried out on the direct torque control (DTC) system. The proposed controller consists of neural network and projection algorithm, in order to tune its parameters adaptively online under environmental variations. The experimental results proved that the performance of the proposed controller...
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 proposes a new indirect adaptive fuzzy controller based on interval type-2 fuzzy logic systems for indirect vector controlled induction motor drive. Type-2 fuzzy controller compared with type-1 fuzzy controller, has the advantage that it can take into account the linguistic uncertainties present in the rules of the estimated models. The proposed control scheme consists of a combination...
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