Robust virtual-vector model predictive control of permanent-magnet motor considering D-Q axis inductance parameter uncertainty

Abstract

To suppress parameter mismatch and improve the output performance of model predictive control (MPC), a new robust virtual-vector MPC strategy is proposed for a five-phase permanent-magnet motor in this study. Firstly, the incremental predictive model is applied to remove the impact of flux mismatch. Then the d-q axis inductance parameter sensitivity of MPC is analysed, which produces the predictive current error between the normal parameter and mismatched parameter. Based on the current error, a new cost function is designed to select the voltage vector more accurately when the d-q axis inductance parameter mismatch occurs. Thus, good robustness to the parameter's variation can be guaranteed. Afterwards, the duty cycle modulation technology is applied to allocate the duration time of two adjacent vector-vectors and zero vector. So the motor current harmonics and torque ripple can be considerably suppressed. Finally, the experimental results are provided to show the effectiveness of this proposed method.