A Composite Model Predictive Control Method of SRMs With PWM-Based Signal for Torque Ripple Suppression

In this article, a novel composite predictive control using PWM control signals is proposed for the torque ripple suppression of the switched reluctance machine (SRM). A PWM modulation method for the asymmetric half-bridge (AHB) converter is introduced to achieve the conversion of the duty cycle to control signals. In the single-phase region, the deadbeat predictive control (DPC) is employed to predict the optimal duty cycle for the conduction phase. The model predictive control (MPC) using PWM control signals predicts the optimal duty cycles for the incoming phase and the outgoing phase in the commutation region. Latin-hypercube sampling (LHS) design is applied to obtain the appropriate duty cycle prediction scheme according to the machine operation state. The response surface model (RSM) is proposed to replace the cost function for achieving online optimization. The performance of the proposed composite predictive control method is evaluated by detailed simulations and experiments under different working conditions. The results show that the proposed control method exhibits better performance in the torque ripple suppression of SRM compared to the conventional MPC.