神经流域编码器在BLDC驱动器中耐受转子偏心
Hubert Milanowski1, Adam K Piłat1
1Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, A. Mickiewicza 30, 30-059 Krakow, Poland.
本研究介绍了无刷直流 (BLDC) 驱动器的磁流编码器,尽管旋转器异常度和动态变化,但提高了旋转器角度精度. 这种新的方法为无编码器电动驱动器控制提供了强大的数据驱动解决方案.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 对于BLDC驱动器的传统霍尔传感器编码器,由于旋转器异心率和动态瞬态,其准确性处于困境.
- 在传统方法中假设理想的正弦流量导致性能降低,导致空隙扭曲.
研究的目的:
- 为BLDC驱动器开发基于磁流的编码器,在具有挑战性的操作条件下实现高精度.
- 为了克服传统角度估计器在旋转器错位和动态瞬态存在时的局限性.
主要方法:
- 用一个带有外源输入的非线性自回归网络 (NARXNet) 作为时间神经观察员.
- 一个基于物理的数据增强框架将实验流量图与动态模拟集成为训练数据生成.
- NARXNet学习了测量流量密度和电转子角度之间的复杂映射.
主要成果:
- 拟议的磁流编码器显示平均角度误差低于2°,95百分位误差低于5°.
- 与经典方法相比,该系统对异常移位和加速瞬态的弹性更强.
- 观察到微不足道的漂移,表明高稳定性和可靠性.
结论:
- 开发的基于NARXNet的磁流编码器提供了一个紧的,数据驱动的传感解决方案,用于强大的,无编码器的电动驱动控制.
- 这种方法在非理想条件下显著提高了BLDC驱动角度估计的准确性和可靠性.
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