Learning and Current Prediction of PMSM Drive via Differential Neural Networks
This work addresses modeling and prediction for PMSM drives, which is important for control applications in robotics and other fields, but appears incremental as it applies an existing DNN method to a specific domain.
The authors tackled the problem of modeling nonlinear systems like permanent magnet synchronous motors (PMSMs) using differential neural networks (DNNs) to predict current trajectories, achieving effective and accurate reconstruction with strong short-term and long-term prediction capabilities and robustness under various load conditions.
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.