Adjustable Model Predictive Control for IPMSM Drives Based on Online Stator Inductance Identification

To suppress the disturbance caused by the parameter mismatch, this article proposes an adjustable model predictive control (AMPC) strategy for an interior permanent-magnet synchronous motor (IPMSM) drives. The key of the proposed method is that an improved model reference adaptive system (IMRAS) is applied to identify online parameters. The traditional proportional-integral (PI) controller in the adaptive law of IMRAS is replaced by a fuzzy logic controller, it can improve the identification accuracy and enhance the robustness of sudden variations in speed reference and load torque. Then, the identified parameters can be used in the AMPC to replace constant parameters. Compared with the traditional model predictive control, the AMPC strategy can effectively decrease motor system current distortion and torque ripple when parameter mismatch occurs. Moreover, the maximum torque per ampere control (MTPA) control strategy based on the formula solution is adopted. And the optimal MTPA operation points can be found automatically by online parameter identification to achieve the minimum amplitudes of the phase current. Finally, the validity and correctness of the proposed method are verified by experiments.