Sensorless Control of Permanent Magnet Synchronous Motors Using Model Predictive Algorithm Based Back-EMF Observer

Back-electromotive force (back-EMF) based sensorless control is widely applied in permanent magnet synchronous motor (PMSM) drives. For high-gain back-EMF observers, the control law often leads to an inherent trade-off between steady-state accuracy and dynamic response. To address this issue, a model predictive control (MPC)-based observer designed in discrete-time domain is proposed in this article. In the studied observer, the optimal control action selected from a predetermined set is adopted to eliminate the current estimation error. Then, the estimated back-EMFs can be extracted from the applied control actions. By leveraging traversing optimization, the proposed method improves the current error and observation accuracy while maintaining fast response and robustness. Experimental results conducted on a 1-kW PMSM platform verify the effectiveness of the proposed observer.