引用本文:王晶,曹柳林,吴海燕,马娜,靳其兵.管式聚合反应器温度分布的动态建模与广义PI控制[J].控制理论与应用,2012,29(8):1043~1050.[点击复制]
WANG Jing,CAO Liu-lin,WU Hai-yan,MA Na,JIN Qi-bing.Dynamic modeling and generalized PI control for temperature distribution of the tubular polymerization[J].Control Theory and Technology,2012,29(8):1043~1050.[点击复制]
管式聚合反应器温度分布的动态建模与广义PI控制
Dynamic modeling and generalized PI control for temperature distribution of the tubular polymerization
摘要点击 2424  全文点击 1285  投稿时间：2012-05-05  修订日期：2012-06-26
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DOI编号  10.7641/j.issn.1000-8152.2012.8.LCTA120451
  2012,29(8):1043-1050
中文关键词  B样条网络  分布参数系统  阳离子聚合反应器  广义PI控制
英文关键词  B-spline network  distributed parameter system  the cationic polymerization reactor  generalized PI control
基金项目  This work was supported by the National Natural Science Foundation of China under Grant (Nos. 60974031, 61174128), and the Fundamental Research Funds for the Central Universities, China (No. ZZ1223).
作者单位E-mail
王晶* 北京化工大学 信息科学与技术学院 jwang@mail.buct.edu.cn 
曹柳林 北京化工大学 信息科学与技术学院 caoll@mail.buct.edu.cn 
吴海燕 北京化工大学 信息科学与技术学院  
马娜 北京化工大学 信息科学与技术学院  
靳其兵 北京化工大学 信息科学与技术学院  
中文摘要
      针对阳离子聚合反应器的温度分布建模与控制问题, 提出了一种基于B样条神经网络的广义PI控制方法. 首先采用B样条复合网络建立分布函数的动态和静态模型, 并基于该模型, 将分布函数的跟踪问题等效为动态权值向量的时间域跟踪问题. 最后给出一种新型的广义PI控制方法，实现对给定温度分布的跟踪控制. 同时, 为了更好地抑制未知干扰、参数摄动以及模型不匹配等问题, 模型权值状态、模型输出与实测温度分布所对应的权值误差都被引入到反馈控制回路, 因此能够大大增强系统的鲁棒性与抗干扰能力. 仿真结果表明该方法的可行性.
英文摘要
      Model of temperature distribution in a tubular polymerization reaction is developed using a B-spline neural network, in which both dynamic and static network are applied to resolve the modeling of distribution function from a high dimensional data set. Based on this dynamic network model, a new-type generalized PI control algorithm has been studied. Then a control problem for distributed system is reduced to a tracking problem of nonlinear dynamic weights, which separates the time and the space ffectively. In order to restrain unknown disturbances and parameter perturbation, not only the weights state of the network model are turn into feedback, but also the output error vector between the model and the real process is introduced at a certain percentage. This provides a feedback channel for the control, and therefore the robustness and anti-disturbance performance is largely enhanced. Simulation results demonstrate the effectiveness of the proposed method.