Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

熊智华; 董进

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Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

Updated：2026-01-06

- Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model
- Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model
- 中国化学工程学报（英文版） 2008年16卷第2期页码：235-240
- Affiliations：
  
  1. Department of Automation, Tsinghua University,Beijing,China,100084
  2. School of Chemical Engineering and Advanced Materials, University of Newcastle, Newcastle upon Tyne, NE17RU, U. K
- Author bio：
- Funds：
  
  the National Natural Science Foundation of China(60404012,60674064);UK EPSRC(GR/N13319 and GR/R10875);the National High Technology Research and Development Program of China(2007AA04Z193);New Star of Science and Technology of Beijing City(2006A62);IBM China Research Lab 2007 UR-Program
- DOI：
  中图分类号：
- 纸质出版：2008
- Accepted：
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熊智华, 董进. Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model[J]. 中国化学工程学报（英文版）, 2008,16(2):235-240.

XIONG Zhihua, ZHANG Jie, DONG Jin. Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model[J]. , 2008, 16(2): 235-240.
熊智华, 董进. Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model[J]. 中国化学工程学报（英文版）, 2008,16(2):235-240. DOI：

XIONG Zhihua, ZHANG Jie, DONG Jin. Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model[J]. , 2008, 16(2): 235-240. DOI：

摘要

Abstract

A batch-to-batch optimal iterative learning control(ILC)strategy for the tracking control of product quality in batch processes is presented.The linear time-varying perturbation(LTVP)model is built for product quality around the nominal trajectories.To address problems of model-plant mismatches

model prediction errors in the previous batch run are added to the model predictions for the current batch run.Then tracking error transition models can be built

and the ILC law with direct error feedback is explicitly obtained.A rigorous theorem is proposed

to prove the convergence of tracking error under ILC.The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.

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