RESEARCH ARTICLE


Observer-Based State Feedback for Enhanced Insulin Control of Type ‘I’ Diabetic Patients



Ali Hariri1, *, Le Yi Wang2
1 DTE Energy, One Energy Plaza, Detroit, Michigan, 48226
2 ECE Department, Wayne State University, Detroit, Michigan, 48202


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Creative Commons License
© Hariri and Wang; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the DTE Energy, One Energy Plaza, Detroit, Michigan, 48226; Tel: 313-235-5188; Fax: 313-235-8052; E-mail: hariria@dteenergy.com


Abstract

During the past few decades, biomedical modeling techniques have been applied to improve performance of a wide variety of medical systems that require monitoring and control. Diabetes is one of the most important medical problems. This paper focuses on designing a state feedback controller with observer to improve the performance of the insulin control for type ‘I’ diabetic patients. The dynamic model of glucose levels in diabetic patients is a nonlinear model. The system is a typical fourth-order single-input-single-output state space model. Using a linear time invariant controller based on an operating condition is a common method to simplify control design. On the other hand, adaptive control can potentially improve system performance. But it increases control complexity and may create further stability issues. This paper investigates patient models and presents a simplified control scheme using observer-based feedback controllers. By comparing different control schemes, it shows that a properly designed state feedback controller with observer can eliminate the adaptation strategy that the Proportional-Integral-Derivative (PID) controllers need to improve the control performance. Control strategies are simulated and their performance is evaluated in MATLAB and Simulink.

Keywords: Biomedical modeling, insulin, diabetes, feedback controller, observer, adaptation..