Model Predictive Torque Control of Six-Phase Switched Reluctance Motors Based on Improved Voltage Vector Strategy

Abstract

To rapidly and accurately establish the model of switched reluctance motors (SRMs) and enhance torque control performance, this article proposes a model predictive torque control (MPTC) strategy based on the optimized voltage vector. First, a fourth-order Fourier series is used to calculate the flux and torque models, and a complete nonlinear model of the SRM is constructed using the Kriging model. Second, the torque characteristics are used to divide the sectors, thereby reducing the number of candidate voltage vectors (CVVs) and effectively decreasing the computational burden of predictive control. Finally, an adaptive adjustment algorithm for the sector boundary angle is proposed, where the position of the sectors is determined based on variations in speed and load. The proposed method reduces torque ripple and enhances dynamic response capability. The effectiveness of this approach is validated through experiments on a 12/10 pole six-phase SRM prototype.