低压铸造机液面加压控制技术研究  

Liquid pressurization control technology of low-pressure casting machine

作　　者：黄飞虎 顾寄南[1] HUANG Fei-hu;GU Ji-nan(Research Center of Mechanical Information,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学制造业信息化研究中心

出　　处：《机电工程》2019年第12期1309-1313,共5页Journal of Mechanical & Electrical Engineering

基　　金：江苏省科技成果转化专项资金招标项目(BA2015026)

摘　　要：针对低压铸造机液面加压系统参数整定困难、压力控制精度不佳的问题,对液面加压系统的组成、工艺以及机理等进行了研究。提出了模糊神经网络在线整定PID参数的方法,设计了2输入、3输出的模糊神经网络;分析了BP学习算法的缺点,改进了模糊神经网络训练方法,使用果蝇算法作为外层循环,BP算法作为内层循环训练模糊神经网络;选择合适的目标函数对模糊神经网络进行了训练,在Matlab中对传统PID、模糊PID和FNN-PID的控制效果进行了仿真分析。研究结果表明:和传统PID控制相比,使用FNN-PID控制器的液面压力最大误差减小了35.6%,平均误差减小了21.6%,有效提高了液面压力的控制精度。Aiming at the difficulties in parameter setting and poor pressure control accuracy of low-pressure casting machine liquid level pressurization system, the composition, process and mechanism of liquid level pressurization system were studied. The PID parameters adjusted online by fuzzy neural network(FNN) was proposed and the fuzzy neural network with 2 input and 3 output was designed. The shortcomings of BP learning algorithm were analyzed and the fuzzy neural network training method was improved. The fruit fly algorithm(FOA)was used as the outer loop and the BP algorithm was used as the inner loop to train fuzzy neural network. The fuzzy neural network was trained by selecting the appropriate objective function. The control effects of traditional PID, fuzzy PID and FNN-PID were simulated and analyzed in MATLAB. The results indicate that compared with the traditional PID control, the maximum error of the liquid level pressure is reduced by 35.6% and the average error is reduced by 21.6% when using FNN-PID controller, which effectively improves the control accuracy of the liquid level pressure.

关 键 词：低压铸造 液面加压 模糊神经网络 PID 果蝇优化算法 

分 类 号：TG233.1[金属学及工艺—铸造] TP273[自动化与计算机技术—检测技术与自动化装置]