Iterative learning control based on integrated dynamic quadratic criterion for batch processes

  • Authors:

  • Li Jia;Jiping Shi;Dashuai Cheng;Luming Cao;Min-Sen Chiu

  • Affiliations:

  • Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;Faculty of Engineering, National University of Singapore, Singapore

  • Venue:
  • LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
  • Year:
  • 2010

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Abstract

An integrated neuro-fuzzy model and dynamic R-parameter based quadratic criterion-iterative learning control is proposed in this paper. Quadratic criterion-iterative learning control with dynamic parameters is used to improve the performance of iterative learning control. As a result, the proposed method can avoid the problem of initialization of the optimization controller parameters, which are usually resorted to trial and error procedure in the existing iterative algorithms. Lastly, example is used to illustrate the performance and applicability of the proposed method.