Abstract

This work considers the problem of automatically controlling the glucose level in insulin dependent diabetes mellitus (IDDM) patients. The objective is to include several important and practical issues in the design: model uncertainty, time variations, nonlinearities, measurement noise, actuator delay and saturation, and real time implementation. These are fundamental issues to be solved in a device implementing this control. Two time-varying control procedures have been proposed which take into consideration all of them: linear parameter varying (LPV) and unfalsified control (UC). The controllers are implemented with low-order dynamics that adapt continuously according to the glucose levels measured in real time in one case (LPV) and by controller switching based on the actual performance in the other case (UC). Both controllers have performed adequately under all these practical restrictions, and a discussion on pros and cons of each method is presented at the end.

Highlights

  • Under normal conditions, blood glucose concentration should be in the interval of 60, 120 mg/dL [1]

  • The absence of insulin released by the pancreas is called insulin dependent diabetes mellitus (IDDM) and produces a higher glucose level in the blood

  • An linear parameter varying (LPV) model can be interpreted as a linear tangent model that moves along the nonlinear system according to its working point

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Summary

Introduction

Blood glucose concentration should be in the interval of 60, 120 mg/dL [1]. There have been no previous attempts to consider all these restrictions in the controller design phase This is important if the final objective is to construct a device to control IDDM automatically. The objective of this work is to test two time-varying algorithm design procedures to control glucose-insulin levels in IDDM, which contemplate all these practical issues: linear parameter varying (LPV) and unfalsified control (UC). This is a first step towards the construction of a practical device which may be applied effectively to patients.

Background
Designs and Simulations
Conclusions and Future Research
Full Text
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