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作 者:崔锦辉 刘雨航 张海燕 CUI Jin-hui;LIU Yu-hang;ZHANG Hai-yan(Leting Bureau of Agriculture and Rural Affairs,Tangshan 063600,China)
出 处:《粮食与饲料工业》2024年第6期44-48,52,共6页Cereal & Feed Industry
摘 要:饲料混合机在工作过程中,会遇到不同种类和粒度的饲料,这些饲料的流动性和密度有所不同,而饲料混合机的复杂系统难以快速响应这些非线性变化,导致饲料混合工序出现误差。为此,研究饲料混合机下料分档运动自动控制技术,建立包括进料、称重以及控制三个环节的饲料混合机下料分档运动PLC控制结构框架,依据皮带电子秤内置压力传感器测量下料重量,并将其与设定目标重量的偏差与偏差变化率作为控制环节PLC控制中模糊RBF神经网络PID控制的输入,将输出的控制量作用于变频器驱动电机,以适应饲料混合机系统的非线性变化,确保各个物料的下料量始终遵循预先设置的饲料生产配方,避免下料错误,完成下料分档运动自动控制。实验结果显示,该技术应用后可确保饲料混合机按照预设配方进行饲料生产,误差极小,且模糊RBF神经网络PID控制的应用可显著提升饲料物料的下料准确程度,提升饲料生产质量。In working,feed mixer usually deals with feeds of varying types and granularities that are different from each other in terms of flow rate and density.The system in feeder mixer can hardly respond to those nonlinear changes so that error may happen in feed mixing procedure.To cope with this problem,this study established a PLC control framework covering feeding,weighing and controlling based on feeding and grading automation technology.The fuzzy radial basis function(RBF)neural network PID control in the controlling PLC was input with the following parameters:feed weight(measured with a pressure sensor built within the electric belt scale),deviation of measured weight from preset weight,and deviation change.On the other hand,the output control parameters acted on a frequency converter-driven motor to make the feed mixer adapt to the nonlinear changes so that all the materials could be fed always as prescribed in the predefined feed production formula.In this way,both feeding and grading could be automatically controlled without any feeding mistake.The experimental results indicated when applied with the technology,the feed mixer could produce feed as per predefined formula with little error.Furthermore,the application of the fuzzy RBF neural network PID could significantly raise feeding accuracy of material feeding and feed production quality.
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