Switched reluctance motor control with artificial neural networks

J.J. Garside, R. Brown, A. Arkadan
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引用次数: 2

Abstract

This paper presents a new control scheme for switched reluctance motor drives based on artificial neural networks (ANN). The ANNs are trained to generate drive circuitry phase current references for velocity reference tracking. A new, application specific ANN architecture is used to improve modeling accuracy. The control ANNs are trained using data from a state space model. The control scheme characteristics are then presented via two case studies. Firstly, a constant velocity control is simulated and a comparison with previously measured results is presented. A velocity reference tracking case study is then presented.
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开关磁阻电机的人工神经网络控制
提出了一种基于人工神经网络的开关磁阻电机驱动控制新方案。训练人工神经网络生成驱动电路相电流参考以进行速度参考跟踪。采用了一种新的、特定于应用的人工神经网络体系结构来提高建模精度。控制人工神经网络使用来自状态空间模型的数据进行训练。然后通过两个案例研究介绍了控制方案的特点。首先对恒速控制进行了仿真,并与实测结果进行了比较。然后给出了一个速度参考跟踪的案例研究。
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