Model Predictive Control Method with Variable Weight Coefficients for Switched Reluctance Motor under Different Operations

In the propulsion and electrical systems of more-electric aircraft (MEA), the switched reluctance motor (SRM) is regarded as one of the key motor topologies due to its simple structure, high-temperature tolerance, wide speed range, and strong fault tolerance. Due to the inherent torque ripple, its application range is limited. Model predictive control (MPC), with its multivariable optimization capability, has been widely applied in torque ripple suppression and efficiency improvement of SRM. Nevertheless, conventional MPC typically adopts fixed weighting coefficients in the cost function, which limits its applicability under varying operations. To address this issue, this article proposes a fuzzy-control-based variable-weight MPC (FCMPC) strategy. By constructing a fuzzy inference system based on a load torque observer, the proposed method dynamically adjusts the weighting coefficient in the cost function according to the motor's operating state, thereby achieving adaptive optimization of the control strategy. The method exhibits excellent performance in both dynamic and steady-state conditions, significantly enhancing the generality and control performance of MPC under various operating conditions. It is particularly suitable for MEA applications that demand high control precision and fast dynamic response.