烘丝机动态特性建模及基于模型的出口水分优化控制方法

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• 英文论文题名：Modeling and Optimal control approach to Outlet Moisture of Cut Tobacco Drier
• 论文作者：王小飞 ; 彭晓燕 ; 杨玉波 ; 彭辉 ; 阮文杰
• 年：2016
• 作者机构：秦皇岛烟草机械有限责任公司;湖南大学机械与运载工程学院;中南大学信息科学与工程学院;
• 论文关键词：烟丝 ; 出口水分 ; RBF-ARX模型 ; 预测控制 ; 干头干尾
• 英文论文关键词：Cut tobacco ; Moisture of outlet ; RBF-ARX model ; Predictive control ; Over-dried cut tobacco during start and finish process of drying operation
• 会议召开时间：2016-12-01
• 会议录名称：中国烟草学会2016年度优秀论文汇编——烟草工业主题
• 语种：中文
• 分类号：TS43
• 学会代码：ZGYG
• 学会名称：中国烟草学会
• 页数：7
• 文件大小：1785k
• 原文格式：D

摘要

烘丝过程存在较强的非线性、不确定性、耦合性和滞后性的特点,目前的烘丝水分控制主要采用PID算法并加以一些前馈补偿及顺序逻辑控制手段,各个控制回路之间相对独立,协调性差,难以做到真正的闭环自动控制。针对烘丝生产过程的不同阶段及不同的生产工艺模式,本文提出了一种能描述依存于进料量与进料水分的过程动态特性的模型,基于所构建的模型提出了一种基于模型预测的烘丝机出口水分控制方法,可保证较高的控制精度要求和控制平稳性、降低干头和干尾料的数量、提高卷烟生产质量。
The cut tobacco drying process possesses strong nonlinearity, uncertainty, coupling and longer time lag. Currently, the PID control algorithm combined with some feedforward-compensation tricks plus sequential logic control strategies have been mainly used in cut tobacco drying process control. However, the control loops are relatively independent each other, so the control system has poor coordination performance, and it is hard to implement real closed-loop automatic control for the drying process. For the different production processes and requirements of the drying process, the models that can describe the feed rate and inlet moisture–dependent dynamic behavior of the process are proposed, and based on the models a predictive control method was designed in this paper to ensure the higher requirements of control accuracy and smoothness. The presented method may largely reduce the amount of over-dried cut tobacco during start and finish process of drying operation, and improve the quality of cigarette.

引文

[1]王艳红,韩光胜.烟草烘丝过程的水分控制[J].北京工业大学学报,2000,26(4):90-95.
    [2]任正云,晏小平,方维岚,夏明东,胡如高.烘丝过程烟丝含水率的MFA控制[J].烟草科技,2005,(6):10-15.
    [3]张建栋,廖国太,张辉,王田田.模糊控制在烟草工业烟丝干燥控制中的应用[J].甘肃科技,2011,27(8):25-29.
    [4]乔宝合.降低烘丝干头干尾量的几种方法[J].科学与财富,2010,(9):141.
    [5]梁魏峰.烘丝控制模式的建立与实现[J].烟草科技,2003,(3):16-17.
    [6]杨明权.改进HAUNI烘丝机控制模式降低烘丝干头干尾量[J].烟草科技,2005,(8):6-9.
    [7]Peng,H.,Ozaki,T.,Haggan-Ozaki,V.,Toyoda,Y.,A parameter optimization method for the radial basis function type models[J].IEEE Trans.on Neural Networks,2003,14(2):432-438.
    [8]Gan,M.,Peng,H.,Chen,L.,A global-local optimization approach to parameter estimation of RBF-type models[J],Information Sciences,2012,197(1):144-190.
    [9]Peng,H.,Wu,J.,Inoussa,G.,Deng,Q.,Nakano,K.,Nonlinear system modeling and predictive control using the RBF nets-based quasi-linear ARX model[J].Control Engineering Practice,2009,17(1):59-66.
    [10]Wu,J.,Peng,H.,Ohtsu,K.,Kitagawa,G.,and Itoh,T.,Ship's tracking control based on nonlinear time series model[J],Applied Ocean Research,2012,36(1):1-11.
    [11]Peng,H.,Kitagawa,G.,Wu,J.,and Ohtsu,K.,Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant[J],Applied Mathematical Modelling,2011,35(7):3541-3551.
    [12]Peng,H.,Nakano,K.,and Shioya,H.,Nonlinear predictive control using neural nets-based local linearization ARX model—Stability and industrial application[J].IEEE Trans.on Control Systems Technology,2007,15(1):130-143.
    [13]Peng,H.,Ozaki,T.,Toyoda,Y.,Shioya,H.,Nakano,K.,Haggan-Ozaki,V.,and Mori,M.,RBF-ARX model based nonlinear system modeling and predictive control with application to a NOx decomposition process[J].(IFAC)Control Engineering Practice,2004,12(2):191-203.