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

›› 2009, Vol. 17 ›› Issue (6): 976-982.

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

周猛飞, 王树青, 金晓明, 张泉灵

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

收稿日期:2009-03-12 修回日期:2009-10-12 出版日期:2009-12-28 发布日期:2009-12-28
通讯作者: WANG Shuqing,E-mail:sqwang@iipc.zju.edu.cn
基金资助:
Supported by the National Creative Research Groups Science Foundation of China (60721062);the National High Technology Research and Development Program of China (2007AA04Z162)

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

ZHOU Mengfei, WANG Shuqing, JIN Xiaoming, ZHANG Quanling

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

Received:2009-03-12 Revised:2009-10-12 Online:2009-12-28 Published:2009-12-28
Supported by:
Supported by the National Creative Research Groups Science Foundation of China (60721062);the National High Technology Research and Development Program of China (2007AA04Z162)

摘要/Abstract

摘要： 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.

关键词: continuous/batch process, model predictive control, event monitoring, iterative learning, soft constraint

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.

Key words: continuous/batch process, model predictive control, event monitoring, iterative learning, soft constraint

周猛飞, 王树青, 金晓明, 张泉灵. Iterative Learning Model Predictive Control for a Class of Continuous/Batch Processes[J]. , 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.

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

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

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