Sequential Model Predictive Torque Control for Six-Phase Machines Without Weighting Factors

Model predictive control has become a powerful and versatile control strategy, particularly effective in controlling multiphase machines. One of its key advantages is the ability to address multiple control objectives by incorporating constraints directly into the cost function. However, this feature typically necessitates the challenging task of tuning weighting factors to balance competing objectives effectively. This paper proposes a sequential model predictive torque control strategy for six-phase induction machines, eliminating the need for weighting factors. The proposed approach simplifies the control design process while maintaining high performance. Simulation and experimental results demonstrate the method's effectiveness, showing accurate torque, flux tracking, and appropriate stator currents regulation in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha -\beta$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x-y$</tex-math></inline-formula> planes. These results highlight the potential of the proposed control strategy for practical implementation in advanced multiphase drive systems.