Untrained artificial neuron based speed control of interior permanent magnet motor drives over full operating speed range

C. Butt, M. Rahman
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Abstract

This paper presents an intelligent speed controller for the interior permanent magnet synchronous motor (IPMSM), based on a single artificial neuron (SAN). Traditional artificial neural network-based motor controllers require extensive offline training, which is both time consuming and requires extensive knowledge of motor behaviour for the specific drive system. In addition, drive behaviour is unpredictable when parameters outside the training set are encountered. The proposed drive system overcomes these limitations by requiring no offline training, is robust under varying operating parameters and is easily adaptable to various drive systems. Drive efficacy is verified in simulation as well as experimentally.
基于非训练人工神经元的内置永磁电机全转速控制
提出了一种基于单个人工神经元(SAN)的内置式永磁同步电动机(IPMSM)智能调速控制器。传统的基于人工神经网络的电机控制器需要大量的离线训练,这既耗时又需要对特定驱动系统的电机行为有广泛的了解。此外,当遇到训练集之外的参数时,驱动行为是不可预测的。所提出的驱动系统克服了这些限制,不需要离线训练,在不同的操作参数下具有鲁棒性,并且易于适应各种驱动系统。通过仿真和实验验证了其驱动效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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