Data-Driven Control of PMSM via Neural Networks With Active Disturbance Learning

Data-driven neural networks offer a novel approach for sensorless control of permanent magnet synchronous motors (PMSMs), which can effectively reduce reliance on motor parameters and simplify modeling processes. However, neural networks trained on idealized data exhibit input feature sensitivity, which may lead to error accumulation ultimately destabilizing the closed-loop control system. To address this issue, this article proposes an innovative training strategy. During data acquisition phase, disturbance noise is deliberately introduced into the field-oriented control (FOC) system to contaminate the training data, which restructures the feature space by integrating perturbation information as a key feature. Subsequently, adversarial training is applied to enhance the robustness of a shallow fully connected neural network (SFNN). The proposed method achieves stable sensorless control in medium-to-high-speed ranges without modifying the network structure or increasing computational density. Experiments on a 0.75-kW, 3000-r/min PMSM platform demonstrate that this method exhibits reliable performance under varying speeds and loads.