Optimization-Based Flux Linkage Control for Torque Ripple Reduction in Switched Reluctance Machines

This work presents an optimization-based control strategy for reducing torque ripple in switched reluctance motors (SRMs), which are often affected by high ripple and acoustic noise. The proposed approach consists of two stages. First, optimal flux waveforms are generated offline as outer flux linkage references using mixed-integer quadratically constrained programming, explicitly incorporating machine constraints. Second, an inner flux control loop tracks these references using an optimal switching sequence model predictive control algorithm. This method mitigates the inherent limitations of conventional inner-loop strategies, which often exhibit poor trade-offs between tracking accuracy and low switching frequency in the presence of nonlinear SRM behavior. The strategy is validated through simulations and experimental tests on an 8/12 three-phase 2.32kW SRM, whose nonlinear flux characteristics are identified via locked-rotor tests using a radial basis function approach. The proposed reference generation and tracking algorithm are benchmarked against state-of-the-art methods. Experimental results demonstrate that the proposed strategy outperforms existing techniques, achieving a 4.3% torque root-mean-square error while reducing switching frequency by up to 60%.