A novel neuro-fuzzy model-based run-to-run control for batch processes with uncertainties

  • Authors:

  • Li Jia;Jiping Shi;Yang Song;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;Faculty of Engineering, National University of Singapore, Singapore

  • Venue:
  • CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
  • Year:
  • 2009

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Abstract

In this paper, a run-to-run control with neuro-fuzzy model updating mechanism is developed. This strategy features the ability to learn from previous batches to obtain iteratively the optimal control profile and adjust the neurofuzzy model parameters. In addition, an updating algorithm guaranteeing the global convergence of the weights of the model is developed based on the Lyapunov approach. As a result, model uncertainties can be handled. Simulation results show that by updating the model from batch to batch, the control profile converges to the corresponding suboptimal one in the subsequent batches.