摘要
电磁阀在织机气流引纬喷射系统中起着关键性的作用。为提高织机的织造效率,解决电磁阀响应滞后的问题,提出基于BP神经网络预测电磁力结合多目标优化遗传算法的优化方法。首先,针对电磁阀衔铁结构及位置的改变造成磁场内磁阻发生复杂性变化导致难以通过理论模型计算出准确的电磁力,利用BP神经网络对电磁阀电磁力进行预测;其次,采用NSGA-II对保存好的BP神经网络预测模型和求得的衔铁质量的数学模型进行优化,获得电磁阀电磁力和衔铁质量的Pareto前沿解;最后,以最接近原衔铁质量的标准选取电磁力最优值,并与原结构电磁力进行比较。结果表明,优化后的电磁力提高了11.5%,但衔铁质量却降低1%。仿真实验也验证了该优化方法的有效性。
The magnetic valve plays a key role in the air-flow weft insertion and injection system of the loom.In order to improve the weaving efficiency of the loom and solve the problem of solenoid valve response lag,an optimization method based on BP neural network prediction of electromagnetic force combined with multi-objective optimization genetic algorithm was proposed.Firstly,in view of the complex change of the magnetic resistance in the magnetic field caused by the change of the armature structure and position of the solenoid valve,which made it difficult to calculate the accurate electromagnetic force through the theoretical model,the BP neural network was used to predict the electromagnetic force of the solenoid valve.Secondly,NSGA-II was used to optimize the preserved BP neural network prediction model and the obtained mathematical model of the armature mass,and the Pareto frontier solution of the electromagnetic force and the armature mass of the solenoid valve was obtained;Finally,the optimal parameter combination was selected according to the standard closest to the original armature quality,and compared with the electromagnetic force of the original structure.The results show that the optimized electromagnetic force increases by 11.5%,but the armature mass decreases by 1%.The simulation experiment also verifies the effectiveness of the optimization method.
作者
沈丹峰
郝祖茂
赵刚
李许锋
SHEN Danfeng;HAO Zumao;ZHAO Gang;LI Xufeng(School of Mechanical and Electrical Engineering,Xi’an Polytechnic University,Xi’an 710048,China;Shaanxi Changling Textile Mechanical&Electronic Technology Co.Ltd.,Baoji 721013,Shaanxi,China)
出处
《西安工程大学学报》
CAS
2023年第2期79-86,共8页
Journal of Xi’an Polytechnic University
基金
陕西省自然科学基金(2022Jq-397)。
