Cascaded Deadbeat Predictive Control for the Unipolar Sinusoidal Excited SRMs Based on the Adaptive Fading Kalman Filter

Abstract

In this paper, a novel cascaded predictive control strategy, endowed with the merits of the deadbeat predictive control solution and Kalman filter framework, is proposed for unipolar sinusoidal excited switched reluctance motors. The primary aim is to augment the dynamic performance of the system while ensuring robustness across various conditions. To achieve this objective, a Kalman filter is meticulously implemented and integrated into the deadbeat predictive current control strategy (DPCC). This integration facilitates a comprehensive consideration of the system’s nonlinear dynamics and measurement noise, thereby reducing the DPCC’s reliance on precise model parameter information. This advancement significantly bolsters the robustness and flexibility of the control system in various operational contexts. Simultaneously, to enhance the dynamic performance of the speed control mechanism and robustness, a deadbeat predictive speed control strategy (DPSC) integrating a novel adaptive fading Kalman filter (AFKF) is proposed. Within the AFKF, a modified fading factor selection method is employed, which contributes to the enhanced convergence speed, thereby optimizing the dynamic performance of the DPSC strategy. Finally, detailed comparative simulations and experiments are undertaken to verify the theoretical framework’s feasibility and to substantiate the performance enhancements ascribed to the proposed control strategy.