基于模糊神经网络逆学习控制的开关磁阻电机转矩脉动最小化

The Fifth International Conference on Power Electronics and Drive Systems, 2003. PEDS 2003. Pub Date : 2003-11-17 DOI:10.1109/PEDS.2003.1283148

Zheng Hongtao, Lin Feng, Liu Lian-gen, Jian Jingping, Xu Dehong

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引用次数: 5

摘要

本文的目的是发展模糊神经网络(FNN)逆学习控制算法，用于srm的转矩脉动最小化。该方法由两个FNN模块组成，它们保留相同的权值。学习FNN模块根据srm的(T-i-/spl theta/)输入输出关系的观测值在线调整权值，形成srm的近似动态逆模型i(T， /spl theta/)。控制FNN模块用于预测SRMs相电流波形，以遵循所需的转矩命令。详细的仿真结果表明，四相SRM具有良好的响应特性。

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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Torque ripple minimization in switched reluctance motors using fuzzy-neural network inverse learning control

The purpose of this paper is the development of fuzzy-neural network (FNN) inverse learning control algorithms for torque-ripple minimization of SRMs. The approach consists of two FNN modules, which spare the same weight values. The learning FNN module is used to adjust the weight values on-line based on observations of the SRMs' (T-i-/spl theta/) input-output relationship in order to form an approximate dynamic inverse model i(T, /spl theta/) of SRMs. The controlling FNN module is used to predict the SRMs phase current waveforms required to follow a desired torque command. Detailed simulation results show good response characteristics for a four-phase SRM.

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来源期刊

The Fifth International Conference on Power Electronics and Drive Systems, 2003. PEDS 2003.

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