Modulated Model Predictive Torque Control for Interior Permanent Magnet Synchronous Machines

Abstract

Thanks to the advancements in the processor industry, the popularity in the industrial applications of Finite control set model predictive control (FCS-MPC) is increasing. FCS-MPC has several advantages, such as high closed-loop bandwidth, the inclusion of the control constraints, and nonlinearities. However, the control signals are directly produced by the predictive controller since no modulator is used. Hence, the system has non-fixed switching frequency, and the maximum achievable switching frequency is limited by the half of the sampling frequency. However, the control goals may suffer from the undesired ripples in case of a noticeable low switching frequency. To eliminate these ripples the sampling period of the system can be reduced. But this increases the computational burden on the processor. To overcome the unwanted oscillations in the control variables and decrease the computational burden on the processor, a modulated model predictive control (M2PC) strategy is proposed in this paper. The M2PC combines the space vector pulse width modulator (SVPWM) and FCS-MPC. Torque of the interior permanent magnet synchronous motor (IPMSM) is controlled with M2PC method. The motor is controlled in a constant torque region with the combination of the M2PC method and maximum torque per ampere (MTPA) control strategy. The comparative results of the conventional MPC method and M2PC method are reported in the paper and the superiority of the M2PC strategy is validated by simulation works. The results demonstrate that the M2PC method significantly reduces total harmonic distortion (THD) in stator currents. Based on the results, the M2PC method provides a better control performance for IPMSMs with significantly reduced torque ripples.