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Thrust Performance Improvement for PMSLM Through Double-Layer Reverse Skewed Coil and WRF-MKH Method
IEEE/ASME Transactions on Mechatronics ( IF 6.4 ) Pub Date : 2020-06-05 , DOI: 10.1109/tmech.2020.3000265
Weitao Wang , Jiwen Zhao , Juncai Song , Fei Dong , Kaifang Zong , Guihua Li

This article is dedicated to achieving the best thrust performance of high thrust and low thrust ripple of permanent magnet synchronous linear motors (PMSLMs), which are used in precision positioning system. To achieve this, a novel PMSLM with double-layer reverse skewed coil (DRSC) is designed and optimized. First, the topological structure of the DRSC-PMSLM is introduced, and the effectiveness of the DRSC is analyzed qualitatively according to the analytic model. Certain hypotheses that are more suited to qualitative analysis can lead to the low accuracy of the analytic model. Therefore, for the subsequent optimization, the weighted random forest (WRF), an enhanced random forest (RF) algorithm by assigning different weights to different regression trees, is proposed to fit the sample data generated by the finite element method to establish a high-precision proxy model. Accuracy test proves that the WRF model has higher accuracy than the standard RF model and the analytical model. Subsequently, a new global optimization algorithm, call the modified Krill Herd (MKH) algorithm is proposed to iteratively optimize the WRF model to obtain the optimal structure parameters. The MKH algorithm is a modified KH algorithm that two strategies are used to improve the convergence rate and avoid premature convergence. Comparative experiments are performed by using genetic algorithm, particle swarm optimization, and standard Krill herd, which prove that the MKH has faster convergence speed and stronger global search capability. The thrust performance of the optimized motor is improved considerably compared with that of the initial motor, which further proves the effectiveness of the MKH used in this article. Finally, the prototype experiment proves that the PMSLM with DRSC has the best thrust performance.

中文翻译:

双层反斜线圈和WRF-MKH方法提高PMSLM的推力性能

本文致力于实现永磁同步线性电动机(PMSLM)的高推力和低推力脉动的最佳推力性能,该产品用于精密定位系统。为此,设计并优化了带有双层反向偏斜线圈(DRSC)的新型PMSLM。首先,介绍了DRSC-PMSLM的拓扑结构,并根据解析模型对DRSC的有效性进行了定性分析。某些更适合定性分析的假设可能会导致分析模型的准确性较低。因此,对于后续优化,加权随机森林(WRF)是一种增强型随机森林(RF)算法,它通过为不同的回归树分配不同的权重,提出了适合有限元方法生成的样本数据以建立高精度代理模型的方法。准确性测试证明,WRF模型比标准RF模型和分析模型具有更高的准确性。随后,提出了一种新的全局优化算法,称为改进的Krill Herd(MKH)算法,以迭代方式优化WRF模型以获得最佳结构参数。MKH算法是一种改进的KH算法,使用两种策略来提高收敛速度和避免过早收敛。通过遗传算法,粒子群算法和标准的磷虾群进行比较实验,证明了MKH具有更快的收敛速度和更强的全局搜索能力。与最初的电动机相比,优化后的电动机的推力性能得到了显着改善,这进一步证明了本文使用的MKH的有效性。最后,原型实验证明带DRSC的PMSLM具有最佳的推力性能。
更新日期:2020-06-05
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