基于模糊神经网络算法的智能控制技术  

Intelligent Control Technology Based on Fuzzy Neural Network Algorithm

作　　者：孙握瑜[1] SUN Wo-yu(School of Information and Artificial Intelligence,Anhui Business College,Wuhu,Ahui 241002,China)

机构地区：[1]安徽商贸职业技术学院信息与人工智能学院,安徽芜湖241002

出　　处：《河北北方学院学报（自然科学版）》2022年第5期1-5,共5页Journal of Hebei North University:Natural Science Edition

基　　金：安徽省高校自然科学研究重点项目(KJ2020A1079);安徽省高校优秀青年人才支持计划项目(gxyq2018237);安徽省质量工程教学研究一般项目阶段性研究成果(2018jyxm0598)。

摘　　要：传统挤出机PID系统对非线性变量参数的控制存在较大偏差,难以满足挤出机的智能控制。建立基于模糊神经网络的PID智能控制系统,实现对温度控制PID参数的整定,通过隶属度函数描述非线性参数集合的相关性,建立模糊变量间的映射关系和模糊控制表,实现对PID参数的微整定,提升了系统对非线性参数的适应性。结果表明采用模糊PID自整定系统达到预定稳定温度所用的条件时间减少,温度波动显著减小,具备了更高的控制准确度和适应性。同时,系统初始参数偏差量化因子较大时,能够使系统快速达到目标温度,提高系统升温效率,但过大偏差值易造成系统温度波动频繁,因此需根据实际工况选定系统输入/输出参数量。The traditional PID system of extruder is difficult to satisfy the intelligent control of extruder due to the large deviation in the control of nonlinear variable parameters.An intelligent PID control system based on fuzzy neural network was established to adjust PID parameters of temperature control.The correlation of nonlinear parameter set was described by membership function,and the mapping relationship between fuzzy variables and fuzzy control table were established to realize the micro-whole of PID parameters and improve the adaptability to nonlinear parameters.The results showed that the less condition time was used to reach the predetermined stable temperature by using the fuzzy PID self-tuning system,and the temperature was significantly smaller with higher control accuracy and adaptability.At the same time,when the larger quantitative factor of the initial parameter deviation of the system could make the system reach the target temperature quickly and improve the heating efficiency of the system,but too large deviation value was easy to cause frequent temperature fluctuation of the system,so it would be necessary to select the input/output parameters of the system according to the actual working conditions.

关 键 词：挤出机 模糊PID系统 智能控制 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]