Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory  

间歇过程综合预测迭代学习控制方法基于二维理论的分析(英文)

作　　者：陈宸 熊智华 钟宜生 

机构地区：[1]Department of Automation, Tsinghua University

出　　处：《Chinese Journal of Chemical Engineering》2014年第7期762-768,共7页中国化学工程学报（英文版）

基　　金：Supported in part by the State Key Development Program for Basic Research of China(2012CB720505);the National Natural Science Foundation of China(61174105,60874049)

摘　　要：Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.Based on the two-dimensional(2D) system theory, an integrated predictive iterative learning control(2D-IPILC)strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control(ILC) and model predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type(Ptype) ILC despite the model error and disturbances.

关 键 词：lterative learning control Model predictive control Integrated control Batch process Two-dimensional systems 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]