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

Author

Wang, Youqing ; Doyle, Francis J., III

Author_Institution

Dept. of Chem. Eng., Univ. of California, Santa Barbara, CA, USA

fYear

2009

fDate

17-19 June 2009

Firstpage

1728

Lastpage

1733

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 beta-cell and the simulation results verify the effectiveness and excellence of this method.

Keywords

adaptive control; iterative methods; learning systems; medical control systems; predictive control; ILC-based MPC; artificial pancreatic beta-cell; direct pattern; indirect iterative learning control; model predictive control; Cardiac disease; Control systems; Diabetes; Insulin; Iterative algorithms; Pancreas; Predictive control; Predictive models; Sugar; Three-term control; glucose control; indirect pattern; iterative learning control; model predictive control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192271

Filename

5192271