Self-Tuning for each PMSM Controller using Big Data based ANN
Self-Tuning for each PMSM Controller using Big Data based ANN
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DOI：
10.23919/ipec-himeji2022-ecce53331.2022.9806923
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Sari Maekawa
中科院分区：
文献类型：
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作者：
Sari Maekawa
研究了基于线性分析的永磁同步电机稳定控制方法；然而，它们很难应用在无法线性化的工作区域和控制条件下。在这种情况下，需要进行反复试验调整以获得所需的特性。在本研究中，我们研究了一种使用大量调整后的 PMSM 参数数据进行人工神经网络学习的方法，并推导出稳定驱动 PMSM 的控制参数。
Methods based on linear analysis have been studied for stable control of permanent magnet synchronous motors; however, they are difficult to apply in the operating regions and under control conditions that cannot be linearized. In such instances, trial and error tuning is required to obtain the desired characteristics. In this study, we investigate a method of learning for an artificial neural network using a large amount of adjusted PMSM parameter data and derive the control parameters to stably drive the PMSM.