A Novel Adaptive Force Ripple Suppressing Method for Double-Sided Switched Reluctance Linear Motor

This article proposes a method to suppress electromagnetic force ripple in switched reluctance linear motors (SRLMs). While the force distribution function (FDF) method is effective, conventional approaches employing fixed functions limit adaptability to varying conditions, and existing adaptive algorithms often exhibit suboptimal online adjustment. To overcome these limitations, this study introduces an adaptive reference trajectory that dynamically adjusts based on mover speed and load. In addition, turn-on/off positions are adaptively modified according to a commutation point selected by the force-per-ampere rate; the selection of the commutation point reduces the current root-mean-square (RMS) and significantly enhances the tracking performance of the system. By transforming the Sigmoid function, the reference trajectory is defined by only two parameters, enabling easy online integration of the adaptive function. Furthermore, a multistep continuous control set model predictive controller (CCS-MPC) with self-correction is adopted as the current controller to improve tracking performance. Simulation and experimental results demonstrate superior regulation performance compared to genetic algorithm (GA) iterative methods and the conventional FDF approach. Compared with the GA algorithm, this method reduces the electromagnetic force ripple by an average of 11.3% and the average current RMS by 2.7%.