Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

周猛飞; 王树青; 金晓明; 张泉灵

doi:10.1016/S1004-9541(08)60305-5

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Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes

Updated：2026-01-06

- Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes
- Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes
- 中国化学工程学报（英文版） 2009年17卷第6期页码：976-982
- Affiliations：
  
  State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University,Hangzhou,China,310027
- Author bio：
- Funds：
  
  Supported by the National Creative Research Groups Science Foundation of China (60721062);the National High Technology Research and Development Program of China (2007AA04Z162)
- DOI：10.1016/S1004-9541(08)60305-5
  中图分类号：
- 纸质出版：2009
- Accepted：
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周猛飞, 王树青, 金晓明, 等. Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes[J]. 中国化学工程学报（英文版）, 2009,17(6):976-982.

ZHOU Mengfei, WANG Shuqing, JIN Xiaoming, ZHANG Quanling. Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes[J]. , 2009, 17(6): 976-982.
周猛飞, 王树青, 金晓明, 等. Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes[J]. 中国化学工程学报（英文版）, 2009,17(6):976-982. DOI： 10.1016/S1004-9541(08)60305-5.

ZHOU Mengfei, WANG Shuqing, JIN Xiaoming, ZHANG Quanling. Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes[J]. , 2009, 17(6): 976-982. DOI： 10.1016/S1004-9541(08)60305-5.

摘要

Abstract

An iterative learning model predictive control(ILMPC) technique is applied to a class of continuous/batch processes.Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances.ILMPC integrates the feature of iterative learning control(ILC) handling repetitive signal and the flexibility of model predictive control(MPC).By on-line monitoring the operation status of batch processes

an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone.The results of an industrial application show that the proposed ILMPC method is effective for a class of continuous/batch processes.

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