基于网络预测的粮食颗粒输送模糊控制系统设计  

Design on fuzzy control system of grain particles transport based on network prediction

作　　者：李阳[1] 裴旭明[1] 胡英杰[2] 

机构地区：[1]郑州轻工业学院机电工程学院,河南郑州450002 [2]西安交通大学机电工程学院,陕西西安710049

出　　处：《郑州轻工业学院学报（自然科学版）》2015年第1期90-94,共5页Journal of Zhengzhou University of Light Industry:Natural Science

基　　金：河南省科技攻关项目(102102210134);河南省教育厅科学技术研究重点项目(12A460013)

摘　　要：针对粮食颗粒管道气力输送系统中风速、压损、料气比等参数之间存在复杂非线性关系,难以建立准确的数学模型以实现闭环控制的问题,以CXLD50吸压混合输送移动式吸粮机为研究平台,设计了用BP神经网络的物料流量预测为反馈环节的模糊控制系统:使用神经网络工具建立物料流量预测模型,以快速方便地进行两相流流量在线测量;模型输出的预测流量与期望流量进行比较后,输入到模糊控制器进行判断推理并输出.仿真结果表明,系统响应迅速,可在50 s内达到理想输出;抗干扰能力强,其误差量稳定在±0.5 kg/s左右,有效改善了离线测量方法的信号反馈滞后现象,提高了输送系统的稳定性.It is difficult to establishe accurate mathematical model to realize the closed-loop control,because of the complex nonlinear relationship among the wind speed,pressure loss,feed-gas ratio and other parame-ters in grain partieles pneumatic transport.To solve this problem,with CXLD50 suction pressure mixed conve-ying mobile grain sucking machine as the research platform,fuzzy control strategy was put forward with the material flow prediction of BP neural network as feedback loop.The system uses the material flow prediction model based on neural network tools which could quickly and conveniently online measured two-phase flow. After comparing the model output flow of prediction and expectation,it was input to the fuzzy controller for judgement and output.Simulation showed that the system has rapid response,achieving ideal output in 50 s and strong anti-interference ability,keeping deviation stable in ±0.5 kg/s so that the fuzzy control system could improve the signal of off-line measurement feedback lag and raise the stability of transport system.

关 键 词：网络预测 气力输送 粮食颗粒输送 模糊控制系统 

分 类 号：TH232[机械工程—机械制造及自动化]