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

Chen Chen; Zhihua Xiong; Yisheng Zhong

doi:10.1016/j.cjche.2014.05.008

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

PROCESS CONTROL | Updated：2026-01-06

- Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory
- Chinese Journal of Chemical Engineering Vol. 22, Issue 7, Pages: 762-768(2014)
- Affiliations：
  
  Department of Automation, Tsinghua University,Beijing,China,100084
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.05.008
  CLC：
- Published：2014
- Accepted：
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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.
DOI：

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. DOI： 10.1016/j.cjche.2014.05.008.

摘要

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.

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