基于模糊神经网络PID的塑料薄膜厚度控制系统设计
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摘要
针对当前塑料挤出行业薄膜厚度控制系统普遍存在的强非线性、大时滞、控制精度低的现状,设计了一种将模糊控制、神经网络与经典PID控制算法结合的塑料薄膜厚度智能控制系统,通过模糊控制与神经网络自学习算法相结合,实现了控制系统PID参数的在线自整定。实验证明:与传统厚度控制方式相比,该系统可以大大降低收敛时间,提高控制精度,将薄膜厚度误差控制在2μm以内。
For the universal phenomenon that the plastic film thickness control system had the characteristic of strong nonlinearity,large time delay,low control precision,a plastic film thickness smart control system was designed combined with fuzzy control,neural network and classic PID control. Through combining fuzzy control with neural network,control parameters of PID were regulated on-line automatically. After field testing,comparing with traditional thickness control,using this system,convergence time is reduced greatly and the control precision is improved,the error of film thickness is reduced to 2 μm.
For the universal phenomenon that the plastic film thickness control system had the characteristic of strong nonlinearity,large time delay,low control precision,a plastic film thickness smart control system was designed combined with fuzzy control,neural network and classic PID control. Through combining fuzzy control with neural network,control parameters of PID were regulated on-line automatically. After field testing,comparing with traditional thickness control,using this system,convergence time is reduced greatly and the control precision is improved,the error of film thickness is reduced to 2 μm.
引文
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[3]胡晓东.吹塑薄膜厚度检测与厚度控制系统设计[J].湖南工程学院学报:自然科学版,2004,14(2):33-35.HU Xiaodong.Thickness of Blow Film and the Design of Its Control System[J].Journal of Hunan Institute of Engineering:Natural Science Edition,2004,14(2):33-35.
[4]王广彪.薄膜厚度在线检测及全自动剖面控制系统介绍[J].塑料制造,2007(12):80-81.WANG Guangbiao.Introduction of Film Thickness on-line Detection and Automatic Section Control System[J].Plastic Manufacture,2007(12):80-81.
[5]胥小勇.高速薄膜流涎机组的控制技术研究[D].南京:南京理工大学,2012.
[6]任宽林.聚酯薄膜生产技术与工艺的研究[D].苏州:苏州大学,2006.
[7]闫娟,杨慧斌.神经网络PID在PLC系统控制中的应用研究[J].计算机仿真,2012,29(1):152-155.YAN Juan,YANG Huibin.PID Algorithm Based on Fuzzy Neural Network Control of PLC System[J].Computer Simulation,2012,29(1):152-155.
[8]李小凡.基于RBF神经网络的PID控制器设计[J].兵工自动化,2009,28(1):45-47.LI Xiaofan.Design of PID Controller Based on RBF Neural Network[J].Ordnance Industry Automation,2009,28(1):45-47.
[9]王越胜,王俊.起爆具生产线中的温度PID控制方案设计[J].杭州电子科技大学学报,2012,32(4):159.WANG Yuesheng,WANG Jun.The Temperature PID Control Design in Booster Production Line[J].Journal of Hangzhou Dianzi University,2012,32(4):159.
[10]陈丽君.基于软测量技术的流体粘度在线测量系统的研究与开发[D].青岛:青岛科技大学,2008:57.
[11]田虎,杨承志.基于DDE和组态软件的控制系统仿真研究[J].自动化仪表,2009,30(2):11-14.TIAN Hu,YANG Chengzhi.Simulating and Researching of Control System Based on DDE and Configuration Software[J].Process Automation Instrumentation,2009,30(2):11-14.
[12]何献忠,刘颖慧,彭华厦,等.基于MCGS组态软件和神经网络自适应PID的p H过程控制[J].电气自动化,2009,31(2):40-42.HE Xianzhong,LIU Yinghui,PENG Huasha,et al.The p H Process Control Based on MCGS and Neural Network Adaptive PID[J].Electrical Automation,2009,31(2):40-42.