Model Predictive Control for PMSM Based on the Elimination of Current Prediction Errors

Predictive control is recognized as a promising control method. Model predictive control (MPC), as one of its branches, has been widely applied in the field of power electronics and motor drives due to the advantage of simple structure and satisfactory dynamic performance. However, MPC is a strongly motor-parameters-dependent method. In practical applications, the challenge exists that motor parameters may change, bringing the error between actual prediction currents and ideal prediction values and thus directly affecting the control performance of the permanent magnet synchronous motor (PMSM) system. To eliminate the current prediction errors (CPEs) and enhance the control performance of the PMSM system, an improved MPC based on the elimination of CPE (MPC-CPE) method is proposed in this article. First, the conventional MPC method and the influence of parameter mismatch are introduced. Then, the MPC-CPE method is put forward where the CPEs corresponding to the eight basic voltage vectors are calculated and an updated cost function is used to choose the optimal one. In this way, the bad influence caused by parameter mismatch can be eliminated effectively. It is worth noting that the calculation process in the MPC-CPE method is relatively simple. Finally, experiments are finished to prove that the MPC-CPE method can significantly circumvent the problem of parameter mismatch and enhance the control performance of the PMSM system on a 1.25-kW experimental platform.