Extended State Observer-Based Predictive Current Control for Dual Three-Phase PMSM with High Dynamic Performance

Model predictive controllers are widely discussed in the field of dual three-phase permanent magnet synchronous motor control. However, conventional predictive current controllers usually suffer from parameter inaccuracies or model uncertainties, resulting in prediction errors and deterioration of control performance. Therefore, in this paper, an extended state observer-based (ESO) model predictive current controller is proposed to effectively improve the dynamic performance of the motor and its robustness to parameters or disturbances. Parameter inaccuracies or model uncertainties are considered to be lumped disturbances and expressed in the modified mathematical model of the motor. Then, with the designed observer estimating the external disturbances in real time, the prediction error is compensated and corrected periodically. Additionally, the parameter design method of the observer is presented to simplify the controller design. Finally, comparative experiments are implemented to sufficiently demonstrate the effectiveness of the proposed method for dynamic performance improvement as well as for parameter robustness. The results show that the proposed method takes only 17μs of computation time with a closed-loop bandwidth of 1839rad/s. In addition, the maximum d-axis following error of the proposed method is only 0.10A in the load dynamics experiments, which is a significant improvement compared to the 0.79A of the traditional proportional-integral controller.