用微分神经网络精准预测永磁同步电机电流变化
Learning and Current Prediction of PMSM Drive via Differential Neural Networks
- 用微分神经网络建模电机非线性动态特性
- 在有无负载下均实现高精度短期与长期预测
- 适合电机控制、机器人等动态系统研究者
连续时间动力系统的学习对理解复杂现象和准确预测至关重要。本研究提出一种基于微分神经网络（DNN）的新方法，用于建模非线性系统——永磁同步电机（PMSM），并预测其电流轨迹。通过在不同负载扰动和空载条件下进行实验验证，结果表明该方法能有效且准确地重构原系统，展现出强大的短期与长期预测能力及鲁棒性。本研究为学习复杂动态数据的内在机制提供了重要见解，未来可拓展应用于天气预报、机器人学和群体行为分析等领域。
原文摘要 · Abstract (English)
Learning models for dynamical systems in continuous time is significant for understanding complex phenomena and making accurate predictions. This study presents a novel approach utilizing differential neural networks (DNNs) to model nonlinear systems, specifically permanent magnet synchronous motors (PMSMs), and to predict their current trajectories. The efficacy of our approach is validated through experiments conducted under various load disturbances and no-load conditions. The results demonstrate that our method effectively and accurately reconstructs the original systems, showcasing strong short-term and long-term prediction capabilities and robustness. This study provides valuable insights into learning the inherent dynamics of complex dynamical data and holds potential for further applications in fields such as weather forecasting, robotics, and collective behavior analysis.
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