An Improved Continuous Control Set Model Predictive Current Control Algorithm for Permanent Magnet Synchronous Motor

Aiming at the issue of poor robustness in continuous control set model predictive current control (CCS-MPCC) algorithm, a class of model predictive controllers based on linear–nonlinear switching extended state observers has been designed. Theoretical derivations demonstrate the structural equivalence between the extended state observer-based deadbeat model predictive current controller and the active disturbance rejection control (ADRC) and the errors introduced into the MPC algorithm’s predictive output by parameters such as motor resistance, flux linkage, and inductance have been calculated. Subsequently, a linear–nonlinear state observer was employed to detect disturbances in the system caused by parameter deviations and the observed disturbance values were fed back into the current loop controller in real time, thereby improving the prediction accuracy of the CCS-MPCC algorithm. Finally, the control algorithm was verified on a 5.5 kW experimental platform of permanent magnet synchronous motor (PMSM). The experimental results show that when the model parameters and actual parameters have deviations, the model predictive current control algorithm based on the linear–nonlinear switching extended state observer has a better speed response effect, and can reduce the harmonic content of the original method by up to 1.03%, 1.25%, and 1.12% under three operating conditions.