Abstract：Switched Reluctance Motors (SRMs) have been extensively utilized in traditional mining machinery, conveyor systems, ventilation equipment, and hoisting devices, owing to their advantages of frequent start-stop capability, high efficiency, robust adaptability, and reliability. Furthermore, under the constraints of rare-earth resource scarcity and the ongoing national new-energy strategy, the rare-earth-free characteristic of SRMs ensures their economic viability, positioning them as an irreplaceable technology in the intelligent development of modern mining industries. Consequently, research on high-performance and cost-effective SRM drive systems holds significant practical value for advancing intelligent mining. The hysteresis control of conventional SRMs results in a high switching frequency, while the nonlinear inductance variation causes significant current ripple during commutation. Model Predictive Control (MPC) is particularly suitable for handling such nonlinear issues. However, traditional finite-set model predictive control (FCS-MPC) for SRMs suffers from the following limitations. (1) Requirement for multiple current sensors, which increases system complexity and cost. Conventional phase-current reconstruction methods based on a single-bus current sensor fail in the two-phase conduction region due to overlapping intervals. The single-current-sensor phase current reconstruction scheme based on high-frequency injection is inefficient at low speeds. (2) Variable switching frequency and suboptimal current tracking. Only one voltage vector combination is selected per control cycle, leading to inconsistent switching frequencies and degraded current-following performance at low control frequencies. This paper proposes a novel dual-vector model predictive current control (DV-MPCC) strategy with a single current sensor. The method integrates vector synthesis techniques. (1) In single-phase conduction regions, a valid vector and a zero vector are combined to calculate the dwell time of the voltage vector combination. (2) In two-phase conduction regions, two valid vectors and a zero vector are synthesized to determine the voltage vector combination dwell time. A bus-current-sensor-based phase current reconstruction scheme is further developed, enabling direct sampling of the conducting phase current in single-phase regions and multi-sampling reconstruction in two-phase regions. A phase-shifting method is employed to minimize reconstruction errors and avoid dead zones. During this process, the causes of errors in this reconstruction method are analyzed, providing a theoretical foundation for error analysis in experiments. Experimental validation was conducted on a three-phase 12/8 SRM using a control platform featuring the TMS320F28379D DSP and AD7606 synchronous sampling chip. The comparisons include the traditional FCS-MPCC method under steady-state current response at 100 r/min and 500 r/min, current response during speed variation from 400 r/min to 600 r/min, current characteristics under load torque variation from 6 N·m to 12 N·m, and torque ripple during commutation. The proposed method demonstrates good fixed switching frequency and superior current tracking performance even at low control frequencies by dynamically selecting multiple switching states per cycle. Accurate phase current reconstruction is achieved in both steady-state and dynamic conditions (e.g., speed variations, load torque transients), with minimal performance degradation compared to multi-sensor systems. Phase-shifting compensation is robust in overlapped regions, ensuring reliable current reconstruction even in dead zones. The proposed method simplifies hardware implementation, enhances control accuracy, and is readily integrated with speed-loop control strategies. It offers a promising solution for industrial SRM drives.
蒯松岩, 魏伟, 吴文杨, 李豫鲁, 胡堃. 基于单电流传感器的开关磁阻电机模型预测电流控制[J]. 电工技术学报, 2026, 41(8): 2578-2589.
Kuai Songyan, Wei Wei, Wu Wenyang, Li Yulu, Hu Kun. Model Predictive Current Control for Switched Reluctance Motors Based on a Single Current Sensor. Transactions of China Electrotechnical Society, 2026, 41(8): 2578-2589.
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2026, Vol. 41 
