Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm
Control Science and Engineering
Volume 3, Issue 2, December 2019, Pages: 29-36
Received: Dec. 15, 2019;
Accepted: Dec. 30, 2019;
Published: Jan. 8, 2020
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Ekhlas Hameed Karam, Computer Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq
Eman Hassony Jadoo, Computer Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, Iraq
Type I diabetic patients is a chronic condition marked by an abnormally large level of glucose in human blood. Persons with diabetes characterized by no insulin secretion in the pancreas (ß-cell) also known as insulin-dependent diabetic Mellitus (IDDM). The treatment of type I diabetes is depending on the delivery of the exogenous insulin to reach the blood glucose level near to the normal range (70-110mg/dL). In this paper, a modified robust linear compensator (MRLC) is suggested to regulate the glucose level of the blood in the presence of the parameter variations and meal disturbance. The Bergman minimal mathematical model is used to describe the dynamic behavior of blood glucose concentration due to insulin regulator injection. Firstly, the robust linear compensator (RLC) is designed based on the linear algebraic method, the simple PD-ADALINE neural network is used to modified the RLC based on the Particle Swarm Optimization technique (PSO) which is used to adjusted the proposed neural network parameters. The simulation part, based on MATLAB/Simulink, was performed to verify the performance of the proposed controller. It has been shown from the results of the effectiveness of the proposed MRLC in controlling the behavior of glucose deviation to a sudden rise in blood glucose.
Ekhlas Hameed Karam,
Eman Hassony Jadoo,
Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm, Control Science and Engineering.
Vol. 3, No. 2,
2019, pp. 29-36.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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