SCI和EI收录∣中国化工学会会刊

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

• PROCESS SYSTEMS ENGINEERING • Previous Articles     Next Articles

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

ZHOU Mengfei, WANG Shuqing, JIN Xiaoming, ZHANG Quanling   

  1. 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)

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

周猛飞, 王树青, 金晓明, 张泉灵   

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

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

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