Research on Model Prediction System of Permanent Magnet Synchronous Motor Based on Parameter Recognition

To address the performance challenges arising from parameter uncertainties or variations in the permanent magnet synchronous motor, a current predictive control method based on the model reference adaptive algorithm is proposed. The reference model in this approach is based on the d-q axis current in the two-phase synchronous rotating coordinate system. By employing the identification adaptive law derived from Popov superstable system design, the identified resistance, inductance, and flux parameters are fed back to the model predictive control system. Online updates of control system parameters are performed using a simulation model. The analysis and simulation results demonstrate the effectiveness of the proposed method in enhancing control system stability in the presence of motor parameter variations.