Abstract
In this paper, fuzzy-based closed-loop controller is applied to obtain a robust controller for blood glucose regulation in type I diabetes mellitus patients. The control technique incorporates expert knowledge about treatment of disease by using Mamdani-type fuzzy logic controller to robustly stabilize the blood glucose concentration in normoglycemic level. Controller performance is considered in terms of its ability to reject the multiple meals, on an averaged nonlinear patient model. Robustness of the controller is tested over a group of patients with model parameter varying considerably from the average model. The controller provides the possibility of more accurate control of blood glucose level in the patient in spite of uncertainty in model parameters and measurement noise. The proposed controller has showed superiority over other classical control techniques. A comparative study is presented with well-known conventional H∞ control technique. Simulation results show the superiority of the proposed scheme in terms of reference tracking, disturbance rejection, and measurement noise in comparison with other approaches.
Additional information
Notes on contributors
Sholeh Yasini
Sholeh Yasini received her Bachelor’s Degree in Electrical Engineering from Sistan and Balouchestan University in Zahedan, Iran in 2003. She then joined The Ferdowsi University of Mashhad (FUM) from which she received the Master’s of Science in Electrical Engineering in 2008. Her research interests include optimal control, reinforcement learning, neural networks, and biomedical signal and systems. Currently she is working on her PhD degree in Electrical Engineering at Ferdowsi University of Mashhad, Mashhad, Iran. E-mail: [email protected]
Ali Karimpour
Ali Karimpour received his M.S. and Ph.D. degrees in Electrical Engineering from The Ferdowsi University of Mashhad (FUM) in 1990 and 2003, respectively. His research interest is in the areas of multivariable control, control structure design, power system dynamics, and electricity market issues. He is currently assistant professor in the department of electrical engineering in Ferdowsi University of Mashhad, Mashhad, Iran. E-mail: [email protected]
Mohammad Bagher Naghibi Sistani
Mohammad Bagher Naghibi Sistani received his Ph.D. degrees in Electrical Engineering from The Ferdowsi University of Mashhad (FUM) in 2006. His research interests include machine learning, artificial intelligence, and biomedical signal and systems. He is currently assistant professor in the department of electrical engineering in Ferdowsi University of Mashhad, Mashhad, Iran. E-mail: [email protected]