Optimized Fuzzy Model Predictive Torque Control With a Dynamic Flux Weighting Factor for Nine-Phase Open-End Winding PMSMs

To address the computational complexity and enhance the dynamic performance of the model predictive torque control (MPTC) for nine-phase open-end winding permanent magnet synchronous motors (OW PMSMs), this article proposes an enhanced MPTC strategy. The first innovation involves optimizing the virtual voltage vectors (VVs) set by reducing the number of candidate vectors from 19 171 to 7, based on an in-depth analysis of the torque error correction and flux ripple suppression capabilities of individual VVs. Second, a fuzzy logic-based dynamic weighting factor is introduced into the cost function, where the torque and the motor speed are used to adaptively modulate the flux weighting factor. By reducing the number of candidate VVs, the computational burden is lowered, enabling more efficient real-time adjustments of the dynamic weighting factor. This adaptive mechanism ensures improved dynamic response and steady-state accuracy under a wide range of operating conditions. Experimental results show that the proposed approach effectively reduces computational time while simultaneously enhancing dynamic response and steady-state performance compared to conventional MPTC.