Indirect iterative learning control: application on artificial pancreatic β-cell

  • Authors:

  • Youqing Wang;Francis J. Doyle

  • Affiliations:

  • Department of Chemical Engineering, University of California, Santa Barbara, CA and Biomolecular Science & Engineering Program, University of California, Santa Barbara, CA and Sansum Diabetes Rese ...;Department of Chemical Engineering, University of California, Santa Barbara, CA and Biomolecular Science & Engineering Program, University of California, Santa Barbara, CA and Sansum Diabetes Rese ...

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

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Abstract

Most existing iterative learning control (ILC) algorithms work in direct pattern; while indirect ILC is an open problem. In this paper, model predictive control (MPC) is chosen as the local controller for processes and ILC is used to update the setpoint for MPC; this novel combination belongs to indirect ILC and is named ILC-based MPC in this paper. Indirect ILC has revealed some advantages compared to direct ILC. The proposed algorithm is validated in artificial pancreatic β-cell and the simulation results verify the effectiveness and excellence of this method.