Cloud-Edge Federated Incremental Learning Framework for PMSM Efficiency Optimization with Lightweight CNN-LSTM Models and OTA Differential Deployment

Permanent magnet synchronous motors (PMSMs) are critical components in industrial automation and energy-efficient drive systems, where dynamic operating conditions, load variations, and equipment aging can significantly affect efficiency. Traditional fixed-parameter controllers or static models often fail to maintain optimal performance under these time-varying conditions. Leveraging advances in cloud computing and distributed learning, this study proposes a cloud-edge federated incremental learning framework specifically designed for PMSM efficiency optimization. The framework integrates lightweight CNN-LSTM models deployed on resource-constrained motor controllers for real-time inference with a cloud-based model repository that performs incremental training and federated aggregation, enabling continuous adaptation to diverse operational scenarios. To ensure safe and efficient large-scale deployment, a secure over-the-air (OTA) differential update mechanism with version management and gray-scale rollout is implemented, minimizing communication overhead while supporting heterogeneous terminal devices. Experimental evaluations conducted on a large-scale PMSM dataset collected from 50 industrial motors show that the proposed framework achieves the best overall performance among all compared models. The Lightweight CNN-LSTM with Incremental Federated Learning and OTA Differential Deployment attains a mean absolute error of 0.412, an RMSE of 0.611, and an R^2 value of 0.973, outperforming both non-federated and conventional baseline models. Furthermore, the proposed architecture reduces parameter size to 109 K and achieves the lowest edge-side inference latency of 8.9 ms, demonstrating its suitability for large-scale deployment on resource-constrained PMSM controllers. These results indicate that the framework not only enables lifelong learning and self-adaptive optimization of PMSM control but also provides a scalable and reliable solution for distributed industrial applications, bridging cloud-edge collaboration, federated learning, and real-time embedded inference.