Virtual-Vector-Based Predictive Torque Control for Six-Phase IM with Reduced Computational Burden and Copper Losses

Finite-control-set model predictive control (FCS-MPC) has become widely accepted in multiphase drives due to its adaptability and robust dynamic response. However, multiphase machines, characterized by low secondary plane ( $x-y$ ) impedance, which is absent in their three-phase counterparts, often exhibit poor current quality under conventional FCS-MPC strategies. This paper proposes a novel predictive torque control (PTC) scheme based on virtual vectors to improve ( $x-y$ ) current regulation by ensuring zero average ( $x-y$ ) voltage generation. The proposed method is compared with classic PTC, which cannot achieve proper ( $x-y$ ) current regulation because it uses a single switching state during the sampling period. Experimental validation includes a detailed comparison of torque and flux behavior, ( $x-y$ ) current mean square error, and total harmonic distortion of the fundamental stator currents, demonstrating the effectiveness of the proposed control approach under various operating conditions. Experimental results confirm reductions of approximately 79.7% in computational burden and 98.4% in copper losses when using PTC-VV compared to classic PTC. An asymmetrical six-phase induction machine is used as the case study.