Torque Ripple Reduction in MPDTC of SRM Drives Using Optimized Switching States and Advanced Torque Sharing Function

The combination of model predictive control and direct torque control approached for switched reluctance motor, multiple switch state combinations should be traversed, which may lead to complex calculations. To solve this problem, a torque compensation‐based enhanced model predictive direct torque control for switched reluctance motor (SRM) with switching states restructuration is proposed. However, effective torque and flux control remain challenging due to highly nonlinear magnetic characteristics and significant torque ripple. The first strategy employs an eight‐sector switching state allocation method for precise position‐based control. The second strategy implements a torque compensation algorithm integrated with a torque sharing function, which selects the phase exhibiting superior torque tracking for compensation. The proposed model is implemented and validated through MATLAB‐based simulations. The performance of the proposed model is compared with existing model predictive direct torque control methods, with a primary focus on minimizing torque ripple. Experimental results show that the proposed strategy can effectively reduce the torque ripple by 38.7%. The proposed control techniques, which analyze variable speed and dynamic operation, are primarily intended for traction motor drive applications. The real‐time hardware controller implements the proposed techniques to enable analysis of additional performance parameters.