Adaptive Linear Predictive Model of an Improved Predictive Control of Permanent Magnet Synchronous Motor Over Different Speed Regions

Model predictive control (MPC) strategy can provide significant benefits for controlling nonlinear systems over classical cascade field-oriented control (FOC). However, the MPC is still in the development stage for a high-performance predictive model. Therefore, the proposed MPC in this article updates the internal linear predictive model at each time step to accurately represent the nonlinear plant of a permanent-magnet synchronous motor (PMSM) plant over different speed regions. In other words, the adaptive discrete linear plant model (ADLPM) is designed to update the current operating conditions of the machine parameters and the equilibrium points of the measured stator currents, speed, and load torque. For the operation in the flux-weakening region, the proposed MPC depends on a performance control algorithm (PCA) to obtain high dynamic performance. In this PCA algorithm, the proposed MPC depends on the modified reference velocity rather than the original reference velocity, which can calculate the required d-axis current directly. Moreover, the proposed cost function is designed directly in terms of the error values of the velocity and d-axis current, which fits the motor performance based on the further constraint of the maximum magnitude of the drawn stator current provided to control the acceleration of the rotor. Finally, comprehensive simulations and experiments have fully demonstrated that the proposed MPC can reduce the speed drop, and torque ripple in response to those of the FOC and traditional MPC strategies.