Quasi-model-based control of type 1 diabetes mellitus

  • Authors:

  • András György;Levente Kovács;Péter Szalay;Dániel A. Drexler;Balázs Benyó;Zoltán Benyó

  • Affiliations:

  • Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary

  • Venue:
  • Journal of Electrical and Computer Engineering - Special issue on Electrical and Computer Technology for Effective Diabetes Management and Treatment
  • Year:
  • 2011

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Abstract

Glucose-insulin models appeared in the literature are varying in complexity. Hence, their use in control theory is not trivial. The paper presents an optimal controller design framework to investigate the type 1 diabetes from control theory point of view. Starting from a recently published glucose-insulin model a Quasi Model with favorable control properties is developed minimizing the physiological states to be taken into account. The purpose of the Quasi Model is not to model the glucose-glucagon-insulin interaction precisely, but only to grasp the characteristic behavior such that the designed controller can successfully regulate the unbalanced system. Different optimal control strategies (pole-placement, LQ, Minimax control) are designed on the Quasi Model, and the obtained controllers' applicability is investigated on two more sophisticated type 1 diabetic models using two absorption scenarios. The developed framework could help researchers engaging the control problem of diabetes.