Research On Multi-Objective Optimization Algorithm Of Tubular Permanent Magnet Linear Motor Based On NSGA-Ⅱ

The Tubular Permanent Magnet Linear Motor(TPMLM)is a straightforwardly designed linear motor that has found extensive use in the industrial sector and doesn’t require any intermediate conversion devices.But because of its low power and efficiency issues,it is susceptible to space harmonics and thrust changes while in operation.Consequently,the creation of high-power,high-efficiency TPMLM is crucial.The multi-objective optimization research of TPMLM is the primary subject of this article,which can be divided into the following three sections:(1)The sensitive parameter analysis method is adopted for parameter selection.In ANSYS Maxwell software,a mathematical model of TPMLM is constructed,which mainly analyzes the magnetic circuit and electromagnetic field and transforms them into a mathematical optimization model.Sensitivity parameter analysis method is used to extract the optimization variables of the motor,set the strong sensitivity parameters as the variables to be optimized,and keep the weak sensitivity parameters unchanged.(2)Regression modeling based on Bagging(Bootstrap aggregating)machine learning method.In this study,Bagging was compared with SVM and demonstrated its superior capability in fitting the motor model,thereby providing a more solid basis for subsequent multi-objective optimization.(3)The present study adopts the improved second-generation non-dominated sorting genetic algorithm(NSGA-Ⅱ)to carry out multi-objective parameter optimization for the electric motor.By utilizing the improved NSGA-Ⅱ algorithm,the optimal combination of structural parameters is obtained,which helps enhance the power and efficiency of the TPMLM.Given the tendency towards local optima and unstable evolutionary processes in the original NSGA-Ⅱ optimization process,the simulated binary crossover(SBX)operator is replaced by the normal distribution crossover(NDX)operator.Additionally,the efficient non-dominated sorting(ENS)method is utilized for non-dominated sorting to ensure the diversity of the optimization solution set.The research findings demonstrate that utilizing the improved NSGA-Ⅱ algorithm for the multi-objective optimization method of TPMLM can effectively balance the power and efficiency performance of the motor.With the utilization of the improved NSGA-Ⅱ algorithm,the output power at a speed of 0.5m/s and a load of 10Ω increases from 410.09 W to 488.62 W,and the average efficiency at a winding resistance value of0.8Ω and a speed of 0.1-1.0m/s increases from 79.84% to 93.17%.Based on the acquired data of structural parameters,simulation experiments utilizing the MTS100 k N universal testing machine were carried out.By means of simulation verification,it was established that the NSGA-Ⅱ algorithm based on NDX and ENS is effective and applicable in the multi-objective optimization design of TPMLM’s power and efficiency.