This paper focuses on the multiobjective design optimization of the permanent magnet synchronous linear motors (PMSLMs), which are applied to a high-precision laser engraving machine. A novel efficient multiobjective design optimization method for a PMSLM is proposed to achieve optimal performances as indicated by high average thrust, low thrust ripple, and low total harmonic distortion at different running speeds. First, based on the finite-element analysis (FEA) data, a regression machine learning algorithm, called an extreme learning machine (ELM), is introduced to solve the calculation modeling problem by mapping out the nonlinear and complex relationship between input structural factors and output motor performances. Comparative simulation experiments conducted using the traditional analytical modeling method and another machine learning modeling method, i.e., support vector machine, confirm the superiority of the ELM. Then, a new bionic intelligent optimization algorithm, called the gray wolf optimizer algorithm, is used to search the best optimization performances and structural parameters by performing iteration optimization calculation for multiobjective functions. Finally, FEA and prototype motor experiments prove the effectiveness and validity of the proposed method.

中文翻译：

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基于极限学习机的PMSLM高效多目标设计优化方法


本文重点研究应用于高精度激光雕刻机的永磁同步直线电机（PMSLM）的多目标设计优化。提出了一种新型高效的 PMSLM 多目标设计优化方法，以在不同运行速度下实现高平均推力、低推力波动和低总谐波失真等最佳性能。首先，基于有限元分析（FEA）数据，引入一种称为极限学习机（ELM）的回归机器学习算法，通过映射输入结构因素与结构因素之间的非线性和复杂关系来解决计算建模问题。输出电机性能。使用传统的分析建模方法和另一种机器学习建模方法，即支持向量机进行的对比仿真实验，证实了ELM的优越性。然后，采用一种新的仿生智能优化算法——灰狼优化器算法，通过对多目标函数进行迭代优化计算来搜索最佳优化性能和结构参数。最后，通过有限元分析和原型电机实验证明了该方法的有效性和有效性。