Iterative Learning Compensation for Parameter Robustness Enhancement of Deadbeat Current Control in FMDRM Drives

The electromagnetic parameters of the flux-modulated doubly-salient reluctance motor (FMDRM) are intricately tied to current and rotor position due to its doubly salient structure. This inherent complexity poses challenges in developing exceptional current controllers. To address this issue, an improved iterative learning compensated deadbeat predictive current control (IL-DPCC) strategy is proposed in this article, which significantly strengthens the controller accuracy and parameter robustness. Initially, the phase inductance variation characteristics and open-winding structure of FMDRM are analyzed in detail. Then, a novel current prediction model is established in the stationary coordinate, enhancing model accuracy and halving switching actions. Additionally, to eliminate the disturbance caused by parameter mismatches, a novel iterative learning observer is integrated into the deadbeat control system. This observer adopts a new learning law that meticulously considers the three-phase disturbances coupling to improve learning speed and reduce storage requirements. Furthermore, the learning-gain operator within this law adapts flexibly according to speed tracking error, which ensures both learning speed and system stability. With the integration of this observer, the proposed strategy achieves superior current quality and parameter robustness. Experimental validation on a three-phase 12/8 FMDRM prototype is provided to affirm the effectiveness of the proposed strategy.