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

doi:10.1016/j.cjche.2014.05.008

›› 2014, Vol. 22 ›› Issue (7): 762-768.DOI: 10.1016/j.cjche.2014.05.008

• PROCESS CONTROL • 上一篇下一篇

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

Chen Chen, Zhihua Xiong, Yisheng Zhong

Department of Automation, Tsinghua University, Beijing 100084, China

收稿日期:2013-12-27 修回日期:2014-03-19 出版日期:2014-07-28 发布日期:2014-08-23
通讯作者: Zhihua Xiong
基金资助:
Supported in part by the State Key Development Programfor Basic Research of China (2012CB720505), and the National Natural Science Foundation of China (61174105,60874049).

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

Chen Chen, Zhihua Xiong, Yisheng Zhong

Department of Automation, Tsinghua University, Beijing 100084, China

Received:2013-12-27 Revised:2014-03-19 Online:2014-07-28 Published:2014-08-23
Supported by:
Supported in part by the State Key Development Programfor Basic Research of China (2012CB720505), and the National Natural Science Foundation of China (61174105,60874049).

摘要/Abstract

摘要： Based on the two-dimensional (2D) systemtheory, 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) andmodel 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: Iterative learning control, Model predictive control, Integrated control, Batch process, Two-dimensional systems

Abstract: Based on the two-dimensional (2D) systemtheory, 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) andmodel 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.

Key words: Iterative learning control, Model predictive control, Integrated control, Batch process, Two-dimensional systems

Chen Chen, Zhihua Xiong, Yisheng Zhong. Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory[J]. , 2014, 22(7): 762-768.

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

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

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