Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm

Ekhlas Hameed Karam; Eman Hassony Jadoo

doi:doi:10.11648/j.cse.20190302.12

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Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm

Ekhlas Hameed Karam, Eman Hassony Jadoo

Published in Control Science and Engineering (Volume 3, Issue 2)

Received: 15 December 2019 Accepted: 30 December 2019 Published: 8 January 2020

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Abstract

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.

Published in	Control Science and Engineering (Volume 3, Issue 2)
DOI	10.11648/j.cse.20190302.12
Page(s)	29-36
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Type I Diabetes, Robust Linear Compensator, Linear Algebraic Method, Bergman Minimal Model, ADALINE Neural Network, Particle Swarm Optimization

References

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[2]	Basher, A. S., design fuzzy control system for blood glucose level for type-1 diabetes melitus patients using ga a simulation study, 2017.
[3]	Sylvester, D. D. and R. K. Munje, Back stepping SMC for blood glucose control of type-1 diabetes mellitus patients. 2017.
[4]	Hassan, S. M. and R. A. Riaz, Closed loop blood glucose control in diabetics. Biomedical Research, 2017. 28 (16): p. 7230-7236.
[5]	Li, C. and R. Hu. PID control based on BP neural network for the regulation of blood glucose level in diabetes. in 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. 2007. IEEE.
[6]	György, A., et al., Quasi-model-based control of type 1 diabetes mellitus. Journal of Electrical and Computer Engineering, 2011: p. 4.
[7]	Colmegna, P. and R. S. Peña, Analysis of three T1DM simulation models for evaluating robust closed-loop controllers. Computer methods and programs in biomedicine, 2014. 113 (1): p. 371-382.
[8]	Mourad, A., G. Keltoum, and H. Aicha, Blood glucose regulation in diabetics using H∞ control techniques. European Journal of Advances in Engineering and Technology, 2015. 2 (5): p. 1-6.
[9]	Morales-Contreras, J., E. Ruiz-Velázquez, and J. García-Rodríguez. Robust glucose control via μ-synthesis in type 1 diabetes mellitus. in 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). 2017. IEEE.
[10]	Abadi, D. N. M., et al., Design of optimal self-regulation Mamdani-type fuzzy inference controller for type I diabetes mellitus. Arabian Journal for Science and Engineering, 2014. 39 (2): p. 977-986.
[11]	Karam, E. H., Reduce the Effect of Disturbance from Linear Unstable Second Order Systems Using Hybrid Controller Scheme. Journal of Engineering and Sustainable Development, 2012. 16 (4): p. 241-255.
[12]	Chen, C.-T., Linear system theory and design. 1998: Oxford University Press, Inc.
[13]	Chen, C.-T., Introduction to the linear algebraic method for control system design. IEEE Control Systems Magazine, 1987. 7 (5): p. 36-42.
[14]	Yacine, A., C. Fatima, and B. Aissa. Trajectory tracking control of a wheeled mobile robot using an ADALINE neural network. in 2015 4th International Conference on Electrical Engineering (ICEE). 2015. IEEE.
[15]	Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft Computing, 22 (2), 387-408.‏
[16]	Coman, S., C. Boldisor, and L. Floroian. Fractional adaptive control for a fractional-order insuline-glucose dynamic model. in 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). 2017. IEEE.

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APA Style

Ekhlas Hameed Karam, Eman Hassony Jadoo. (2020). Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm. Control Science and Engineering, 3(2), 29-36. https://doi.org/10.11648/j.cse.20190302.12

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ACS Style

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 Sci. Eng. 2020, 3(2), 29-36. doi: 10.11648/j.cse.20190302.12

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AMA Style

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 Sci Eng. 2020;3(2):29-36. doi: 10.11648/j.cse.20190302.12

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@article{10.11648/j.cse.20190302.12,
  author = {Ekhlas Hameed Karam and Eman Hassony Jadoo},
  title = {Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm},
  journal = {Control Science and Engineering},
  volume = {3},
  number = {2},
  pages = {29-36},
  doi = {10.11648/j.cse.20190302.12},
  url = {https://doi.org/10.11648/j.cse.20190302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20190302.12},
  abstract = {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.},
 year = {2020}
}

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TY  - JOUR
T1  - Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm
AU  - Ekhlas Hameed Karam
AU  - Eman Hassony Jadoo
Y1  - 2020/01/08
PY  - 2020
N1  - https://doi.org/10.11648/j.cse.20190302.12
DO  - 10.11648/j.cse.20190302.12
T2  - Control Science and Engineering
JF  - Control Science and Engineering
JO  - Control Science and Engineering
SP  - 29
EP  - 36
PB  - Science Publishing Group
SN  - 2994-7421
UR  - https://doi.org/10.11648/j.cse.20190302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

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Author Information

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

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Ekhlas Hameed Karam, Eman Hassony Jadoo. (2020). Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm. Control Science and Engineering, 3(2), 29-36. https://doi.org/10.11648/j.cse.20190302.12

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ACS Style

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 Sci. Eng. 2020, 3(2), 29-36. doi: 10.11648/j.cse.20190302.12

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AMA Style

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 Sci Eng. 2020;3(2):29-36. doi: 10.11648/j.cse.20190302.12

Copy | Download

@article{10.11648/j.cse.20190302.12,
  author = {Ekhlas Hameed Karam and Eman Hassony Jadoo},
  title = {Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm},
  journal = {Control Science and Engineering},
  volume = {3},
  number = {2},
  pages = {29-36},
  doi = {10.11648/j.cse.20190302.12},
  url = {https://doi.org/10.11648/j.cse.20190302.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20190302.12},
  abstract = {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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Design Modified Robust Linear Compensator of Blood Glucose for Type I Diabetes Based on Neural Network and PSO Algorithm
AU  - Ekhlas Hameed Karam
AU  - Eman Hassony Jadoo
Y1  - 2020/01/08
PY  - 2020
N1  - https://doi.org/10.11648/j.cse.20190302.12
DO  - 10.11648/j.cse.20190302.12
T2  - Control Science and Engineering
JF  - Control Science and Engineering
JO  - Control Science and Engineering
SP  - 29
EP  - 36
PB  - Science Publishing Group
SN  - 2994-7421
UR  - https://doi.org/10.11648/j.cse.20190302.12
AB  - 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.
VL  - 3
IS  - 2
ER  -

Copy | Download