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Comparative study of ANN DTC and conventional DTC controlled PMSM motor

机译:ANN DTC与传统DTC控制的PMSM电动机的比较研究

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In this paper an Artificial Neural Network (ANN) algorithm is presented in order to solve the problems associated with the conventional DTC approach. In order to improve the performances of the DTC controlled PMSM and to reject the disturbances, an ANN algorithm is used. This intelligent artificial technique is used to select the optimal voltage vector. In order to reduce the torque and flux ripples, the hysteresis comparators and the switching table have been substituted by the ANN technique. Simulation using Matlab/Simulink environment and experimental results around the Dspace-1104, are presented to test the performances of this approach. Simulation and experimental results show the high performances of the ANN-DTC compared to the conventional DTC; in particular the reduction of the ripples in torque and flux. (C) 2019 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
机译:为了解决与传统DTC方法相关的问题,本文提出了一种人工神经网络(ANN)算法。为了提高DTC控制的PMSM的性能并消除干扰,使用了ANN算法。这种智能的人工技术用于选择最佳电压矢量。为了减少转矩和磁通波动,磁滞比较器和开关表已被ANN技术取代。提出了使用Matlab / Simulink环境进行的仿真以及Dspace-1104周围的实验结果,以测试这种方法的性能。仿真和实验结果表明,与传统DTC相比,ANN-DTC具有较高的性能。特别是减小转矩和磁通的波动。 (C)2019国际模拟数学与计算机协会(IMACS)。由Elsevier B.V.发布。保留所有权利。

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