A Simplified Model Predictive Control for Asymmetrical Six-Phase Induction Motors That Eliminates the Weighting Factor

Abstract

The conventional model predictive control (MPC) is an attractive control scheme for the regulation of multiphase electric drives, since it easily exploits their inherent advantages. However, as the number of phases increases, the MPC’s complexity increases exponentially, posing a high computational burden. Additionally, the MPC still presents other issues related to the weighting factor design in the cost function. Accordingly, this paper proposes a low-complexity hysteresis model predictive current control (HMPCC) that can significantly reduce the computational burden, improve the motor’s performance, and completely avoid the weighting factor design. The proposed method is a hybrid control method, consisting of two distinct controls that complement one another. The hysteresis control is used to reduce the number of iterations per sampling period, thereby reducing the computational effort required to choose the voltage vector that actively produces torque/flux, and nullifying the weighting factor requirement. Finally, the MPC is used to improve the torque and current quality. The effectiveness of the proposed method is verified through experimental data, and the results emphasize the improvement of the proposed HMPCC scheme.

Model-Free Predictive Current Control of Five-Phase PMSM Drives

“…Generally, the high-performance control strategies of multiphase motor drives are derived from traditional three-phase counterparts, e.g., field-oriented control [4] and direct torque control [5]. Meanwhile, finite control set-model predictive control (MPC) has been widely investigated in conjunction with motor drives [6][7][8]. Model predictive current control and model predictive torque control are two typical manners of MPC.…”