Machine Learning-Based Model Predictive Control for End Effect Mitigation in PMLSM

This study presents a novel compensation approach designed to mitigate end effects in Permanent Magnet Synchronous Linear Motors (PMLSM), with the aim of improving torque precision and enhancing system stability. Although PMLSMs are widely recognized for their exceptional performance in high-precision applications, they often experience performance degradation due to the non-uniform distribution of the magnetic field, particularly at the motor’s ends. Existing compensation methods frequently encounter difficulties in adapting to varying operational conditions, resulting in suboptimal performance. To overcome these challenges, we propose the integration of a Support Vector Regression (SVR) model with Model Predictive Control (MPC) in a unified control framework. This method leverages finite element modeling, machine learning, and predictive control strategies to optimize motor behavior. Specifically, a high-fidelity PMLSM model is developed using ANSYS Maxwell, which is employed to simulate the magnetic field distributions and identify areas of non-uniformity. The SVR model is then utilized to predict the motor’s position and torque, while also calculating the required current for the next time step. This predicted current is subsequently fed into the MPC framework, which facilitates real-time torque compensation, ensuring improved motor performance. Experimental results validate the proposed approach, showing a significant reduction in torque ripple and a notable enhancement in motor stability compared to conventional methods. The findings highlight the potential of combining machine learning techniques with predictive control to not only improve PMLSM performance but also to enhance the reliability and efficiency of high-precision motor systems. This research opens up new possibilities for the application of such advanced control strategies in next-generation precision-driven technologies.