基于单神经元神经网络的电流控制永磁无刷直流电动机驱动

Nasim Mahmud, Protik Biswas
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引用次数: 4

摘要

永磁无刷直流(PMBLDC)电机在现代自动化工业中有着巨大的工业应用范围。本文提出了一种基于人工神经网络的PMBLDC电机驱动方法,利用基于单个神经元的神经网络避免了神经网络的计算复杂度。本文研究了两种具有不同激活函数的神经网络。一种是递归神经网络(RNN),另一种是基于前馈-反传播(FFBP)的神经网络。在不同的动态条件下,比较了基于人工神经网络的驱动与传统自适应PI控制器的性能。考虑到起动特性,所提出的驱动系统与传统驱动系统相比没有性能偏差。但在转速动态变化的情况下,采用基于神经网络的单神经元控制器的PMBLDC电机驱动器的暂态电流响应较传统驱动器有所缓和。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single Neuron ANN Based Current Controlled Permanent Magnet Brushless DC motor Drives
Permanent Magnet Brushless DC (PMBLDC) motor has a huge scope of industrial application in modern automated industries. In this paper, an artificial neural network (ANN) based PMBLDC motor drives are proposed and the computational complexities of the neural network is avoided by using single neuron based neural network. Two types of neural network with different activation functions are considered in this paper. One is recurrent neural network (RNN) and another is feed forward back propagation (FFBP) based neural network. The performance of these proposed ANN based drives with respect to conventional adaptive PI controller is compared for different dynamic conditions. Considering starting characteristics there is no performance deviation among the proposed drives with respect to conventional drive system. But in case of dynamic speed changing condition, the transient current response of PMBLDC motor drive for proposed single neuron ANN based controller is moderated as compared to conventional drive.
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