基于ANN的在线参数校正用于PMSM控制,使用球体解码算法
Joseph O Akinwumi1, Yuan Gao1, Xin Yuan2
1School of Engineering, University of Leicester, Leicester LE1 7RH, UK.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一个人工神经网络 (ANN),以弥补永久磁同步电机 (PMSM) 驱动器的参数变化. 该ANN增强控制器的性能和稳定性对流量链接和电感的不确定性.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 永磁同步电机 (PMSM) 驱动器由于在运行不确定性下的流量连接和感应等参数变化而容易降低性能.
- 准确的参数估计对于保持PMSM驱动器的最佳性能和效率至关重要.
研究的目的:
- 开发和评估基于人工神经网络 (ANN) 的方法,用于在线估计和补偿PMSM驱动器中的参数不匹配.
- 提高PMSM驱动器的稳定性和性能,以应对流量连接和电感的变化.
主要方法:
- 使用基于球体解码算法的模型预测控制 (SDA-MPC) 来生成数据,参数不匹配范围为±50%.
- 一个ANN的离线培训,以建立测量特征和参数估计之间的映射.
- 在线部署训练有素的ANN,在SDA-MPC框架内动态更新控制器参数.
主要成果:
- 在各种参数不匹配条件下,基于ANN的补偿改善了电流跟踪并减少了总波扭曲 (THD).
- 在特定的场景中,特别是过高估计的电感时,THD与名义运行相比可能会增加.
- 该ANN展示了自适应能力,在参数正常化后,控制器恢复到基线性能.
结论:
- 使用ANN的数据驱动适应为PMSM驱动器提供了增强的稳定性,最小的计算开销.
- 拟议的方法显示了在现实的运行不确定性下提高PMSM驱动性能的潜力.
- 需要进一步的研究,包括硬件循环测试和温度效应分析.
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