Model Predictive Current Control and Direct Torque Control for SRM With Cost Function‐Based Switching States to Optimize System Performance

This paper presents a model predictive current control (MPCC) strategy for switched reluctance motors (SRMs) aimed at mitigating the computational complexity and torque ripple issues commonly associated with conventional control methods. To address these challenges, an integrated MPCC and direct torque control (DTC) framework is proposed, leveraging active voltage vectors (VVs) that incorporate rotor position‐dependent sector selection. This approach enables accurate prediction of phase current trajectories across various switching states. A key feature of the proposed method is the sector‐based allocation, which limits the number of active switching states to four, substantially improving its real‐time applicability. The control algorithm minimizes a cost function that penalizes both current tracking error and switching effort, thereby ensuring precise current regulation while reducing unnecessary switching transitions. Furthermore, the integrated MPCC‐DTC algorithm enhances torque control by employing Model Predictive Phase Compensation to identify the motor phase with optimal torque tracking capability. The proposed model was developed and validated using MATLAB, and the simulation results were verified for torque regulation under dynamic conditions. Experimental results demonstrate that the proposed hybrid control strategy significantly reduces computational overhead while achieving superior torque ripple suppression compared to conventional predictive controllers.