【目的】针对电励磁双凸极电机传统模型预测电流控制(MPCC)存在的电流谐波大、参数依赖性强的缺陷，提出一种改进双矢量模型预测电流控制(IDV-MPCC)策略，并结合在线参数辨识以提高控制鲁棒性。【方法】该策略通过矢量扇区的快速定位，将传统遍历算法中的寻优次数从18次减少为仅需4次，同时重新划分扇区并给出了新的矢量组合选取表，有效降低电流预测误差及纹波；针对参数敏感性问题，采用模型参考自适应系统(MRAS)对电枢绕组自感与互感进行在线辨识，并且将辨识结果实时输入到预测模型中实现在线修正，以提高系统鲁棒性。【结果】IDV-MPCC方法显著降低了电流谐波与转矩脉动，同时基于MRAS的在线辨识有效抑制了参数失配引起的预测误差。【结论】试验结果验证了本文所提MRAS-IDV-MPCC方法能够有效提升控制性能，同时降低系统的计算负担。

[Objective] This study addresses the limitations of conventional model predictive current control (MPCC) for doubly salient electromagnetic machine, including excessive current fluctuations and strong parameter dependency. An improved dual-vector model predictive current control (IDV-MPCC) strategy integrated with online parameter identification is proposed to enhance control robustness. [Methods] The proposed strategy achieved rapid sector localization of vectors, reduced the number of optimizations from 18 in traditional exhaustive search algorithms to just 4. This was accomplished by redefining the sectors and providing a new table for selecting vector combinations, which significantly reduced current prediction errors and ripples. To address parameter sensitivity issues, the model reference adaptive system (MRAS) was employed to perform online identification of the self-inductance and mutual inductance of the armature windings, and the identified results were fed back into the prediction model in real-time to correct it, thereby enhancing system robustness. [Results] The IDV-MPCC method significantly reduced current harmonics and torque ripples, while the MRAS-based identification effectively suppressed prediction errors caused by parameter mismatches. [Conclusion] The experimental results have verified that the proposed MRAS-IDV-MPCC method can effectively improve the control performance while reducing the computational burden of the system.