Low Complexity Model Predictive Control for Asymmetrical Six-Phase Motors

Model predictive control (MPC) has emerged as a promising option for multiphase electric drives. Its application in multiphase machines has led to intensive research efforts, aiming to address various challenges and, ultimately, enhance drives performance. Despite the intensive progress made in this field, there are still important challenges to be handled, such as regulation of the harmonic currents and high computation burden. This paper proposes a low complexity predictive control for asymmetrical six-phase induction motor (A6PIM), which utilizes an enhanced version of deadbeat control (DB), leveraging both the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha-\beta$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x-y$</tex> subspaces to determine the optimal control action with a reduced number of computations. Experimental results confirm the effectiveness of the proposed control, demonstrating a reduction in the execution time by 58.9% when compared to classical MPC. Such reduction contributes to an overall enhancement in the motor performance and efficiency.