基于神经网络的DC-DC变换器自适应在线训练控制方法

H. Maruta, M. Motomura, F. Kurokawa
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引用次数: 5

摘要

提出了一种基于神经网络的直流变换器自适应控制方法。为了获得高性能的dc-dc变换器,要求控制方法适应条件的变化。本研究采用神经网络控制来改善dc-dc变换器的暂态响应。它与传统的PID控制协同工作,实现了高自适应控制。神经网络用在线获取的数据进行训练。因此,神经网络控制可以动态适应输入的变化。通过修改PID控制中的参考值来实现自适应。仿真结果验证了该方法的有效性。结果表明,该方法有助于实现这种自适应控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Neural Network Based Control Method with Adaptive On-Line Training for DC-DC Converters
This study presents a novel adaptive control based on a neural network for dc - dc converters. The control method is required to adapt to changes of conditions to obtain high performance dc-dc converters. In this study, the neural network control is adopted to improve the transient response of dc-dc converters. It woks in coordination with a conventional PID control to realize a high adaptive method. The neural network is trained with data which is obtained on-line. Therefore, the neural network control can adapt dynamically to change of input. The adaptation is realized by the modification of the reference in the PID control. The effect of the presented method is confirmed in simulations. Results show the presented method contributes to realize such adaptive control.
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