Model predictive torque control based on virtual vectors for six-phase induction machines

Abstract

Model Predictive Control (MPC) has become an effective control strategy, particularly in Multiphase Induction Machines (MIMs). Unlike their three-phase counterparts, MIMs have additional degrees of freedom, known as (x − y) voltages or currents. MPC can integrate diverse constraints through a predefined cost function to regulate (x − y) components, but this can come at the cost of disturbing the flux and torque production. To address this challenge, a new approach has been introduced in this paper: Model Predictive Torque Control using Virtual Vectors (PTC-VV) for a six-phase IM. This approach aims to regulate copper losses in the (x − y) plane, which classic PTC cannot achieve using a single switching state during the sampling period. This work demonstrates the effectiveness of using virtual vectors in torque control for six-phase IMs through comprehensive simulation studies. The PTC-VV approach provides robust reference tracking for torque, flux, and stator (α − β) and (x − y) current regulations. This results in enhanced efficiency and adaptability of the control system, marking a notable advancement in PTC techniques. Additionally, this approach reduces the (x − y) currents in six-phase IMs.