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Modeling and Simulating for Artificial Neural Network-Based Direct Torque Control for Induction Motor Drive

Wang Qunjing Chen Quan Jiang Weidong Hu Cungang (Hefei University of Technology Hefei 230009 China)  
As a prospective control scheme—direct torque control (DTC) has a control error caused by the time delays required for the lengthy computations. However, the neural network, with its parallel computation and robust capabilities, offers a promising means to minimize the error. This paper presents an artificial neural network-based (ANN) DTC scheme for an induction motor drive. The neural networks used in this paper are fixed-weight networks and supervised networks. According to the features of DTC, the local training strategy is adopted in this paper. Finally, computer simulations of the designed DTC system are presented and discussed. The experimental results indicate that ANN-based DTC may be a feasible alternative to realization of a high performance DTC system.
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