Efficient Optimization Design Method of PMSLM Based on Deep Adaptive Ridge Regression With Embedded Analytical Mapping Function

Abstract

To improve the thrust performance of permanent magnet synchronous linear motor (PMSLM), novel deep adaptive ridge regression with embedded analytical mapping function (EAMF-RR) is proposed to calculate the thrust performance of PMSLM quickly and accurately, and combined with the EAMF-RR, a whale optimization algorithm (WOA) is introduced to optimize the PMSLM structure. First, the PMSLM's thrust is analyzed by the analytical modeling (AM) to determine the variation range of structural design parameters. Based on the variation range, a finite-element sample database is established for the EAMF-RR. Then, EAMF derived from AM, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor algorithm, and WOA are integrated into RR to establish deep adaptive EAMF-RR model. Comparative simulation experiments conducted by using another two machine learning methods, i.e., extreme learning machine and weighted random forest, confirm the superiority of the EAMF-RR. Finally, WOA is reused to optimize the thrust performance of PMSLM, and prototype experiments prove the effectiveness of the proposed optimization method.