Modulated Model Predictive Control of Permanent Magnet Synchronous Motors with Improved Steady-State Performance

Abstract

Finite Control Set Model Predictive Control (FCS-MPC) is an optimal control strategy that predicts the future trends of the control goals by assessing the discrete-time model of the system. FCS-MPC has many advantages, such as it has a fast dynamic response, and nonlinearities can be controlled by the customized cost function. Besides the featured benefits of the FCS-MPC strategy, the ripple in the output variable (in most cases, control variable) may be problematic due to the uncontrolled switching frequency. For that reason, the MPC-based closed-loop strategy offers a better regulation performance at high-sampling frequency. However, the selection of a low sampling rate causes an unpleasant distortion or poor power quality. A modulated model predictive control method is proposed in this work to suppress the unwanted distortion in the control variable. In the proposed method, a space vector modulator is integrated into the FCS-MPC-based control method to attain a fixed-switching frequency. By doing so, the distortions and unwanted harmonics are significantly decreased. In this paper, a modulated model predictive control (M2PC) method is proposed for controlling the permanent magnet synchronous motor. The proposed method calculates the dwell-time of the modulator stage by assessing the multi-objective cost function. The noticeable lower distortions in the stator currents are obtained by the proposed routine. All theoretical concepts are verified by extensive simulations. Based on the simulation results, the proposed method provides a better control performance for permanent magnet synchronous motors (PMSM). Furthermore, the proposed modulated MPC strategy offers superior steady-state performance compared to the conventional MPC method in all regards.