DESIGN OF MODIFIED SECOND ORDER SLIDING MODE CONTROLLER BASED ON ST ALGORITHM FOR BLOOD GLUCOSE REGULATION SYSTEMS
Ekhlas H. KARAM
ek_karam@yahoo.com* Mustansiriyah University, College of Engineering, Computer Engineering Department, Palestine Street, 14022, Baghdad (Iraq)
Eman H. JADOO
Mustansiriyah University, College of Engineering, Computer Engineering Department, Palestine Street, 14022, Baghdad (Iraq)
Abstract
The type1 of diabetes is a chronic situation characterized by abnormally high glucose levels in the blood. Persons with diabetes characterized by no insulin secretion in the pancreas (ß-cell) which also known as insulin-dependent diabetic Mellitus (IDDM). In order to keep the levels of glucose in blood near the normal ranges (70-110mg/dl), the diabetic patients needed to inject by external insulin from time to time. In this paper, a Modified Second Order Sliding Mode Controller (MSOSMC) has been developed to control the concentration of blood glucose levels under a disturbing meal. The parameters of the suggested design controller are optimized by using chaotic particle swarm optimization(CPSO) technique, the model which is used to represent the artificial pancreas is a minimal model for Bergman. The simulation was performed on a MATLAB/ SIMULINK to verify the performance of the suggested controller. The results showed the effectiveness of the proposed MSOSMC in controlling the behavior of glucose deviation to a sudden rise in blood glucose.
Keywords:
Type I Diabetes, Second Order Sliding Mode Control, Chaotic Particle Swarm Optimization, BEM modeReferences
Abu-Rmileh, A., & Garcia-Gabin, W. (2011). Smith predictor sliding mode closed-loop glucose controller in type 1 diabetes. IFAC Proc. Vol., 18(PART 1), 1733–1738.
DOI: https://doi.org/10.3182/20110828-6-IT-1002.01213
Google Scholar
Alam, W., Ali, N., Ahmad, S., & Iqbal, J. (2018). Super twisting control algorithm for blood glucose regulation in type 1 diabetes patients. In 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST) (pp. 298–303). IEEE. http://doi.org/10.1109/IBCAST.2018.8312239
DOI: https://doi.org/10.1109/IBCAST.2018.8312239
Google Scholar
Amet, L., Ghanes, M., & Barbot, J-P. (2012). HOSM control under quantization and saturation constraints: Zig-Zag design solutions. In 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) (pp. 5494–5498). Maui, HI. http://doi.org/10.1109/CDC.2012.6426197.
DOI: https://doi.org/10.1109/CDC.2012.6426197
Google Scholar
Basher, A.S. (2017). Design fuzzy control system for blood glucose level for type-1 diabetes melitus patients using ga a simulation study (Msc. Thesis). The Islamic University (Gaza).
Google Scholar
Bergman, R.N., Phillips, L.S., & Cobelli, C. (1981). Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. The Journal of clinical investigation. 68(6), 1456–1467.
DOI: https://doi.org/10.1172/JCI110398
Google Scholar
Djouima, M., Azar, A.T., Drid, S., & Mehdi, D. (2018). Higher Order Sliding Mode Control for Blood Glucose Regulation of Type 1 Diabetic Patients. International Journal of System Dynamics Applications (IJSDA), 7(1), 65–84.
DOI: https://doi.org/10.4018/IJSDA.2018010104
Google Scholar
Fisher, M.E. (1991). A semiclosed-loop algorithm for the control of blood glucose levels in diabetics. IEEE Trans Biomed Eng, 38(1), 57–61.
DOI: https://doi.org/10.1109/10.68209
Google Scholar
Garcia-Gabin, W., Zambrano, D., Bondia, J., & Vehí, J. (2009). A sliding mode predictive control approach to closed-loop glucose control for type1 diabetes. IFAC Proceedings Volumes, 42(12), 85–90.
DOI: https://doi.org/10.3182/20090812-3-DK-2006.0046
Google Scholar
Hadi, E.A. (2019). Multi Objective Decision Maker for Single and Multi Robot Path Planning (MSc. thesis). University of Technology (Iraq).
Google Scholar
Kaveh, P., & Shtessel, Y.B. (2006). Blood Glucose Regulation in Diabetics Using Sliding Mode Control Techniques. In 2006 Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory (pp. 171–175). Cookeville, TN. http://doi.org/10.1109/SSST.2006.1619068
DOI: https://doi.org/10.1109/SSST.2006.1619068
Google Scholar
Levant, A. (1993). Sliding order and sliding accuracy in sliding mode control. Int J Control, 58(6), 1247–63.
DOI: https://doi.org/10.1080/00207179308923053
Google Scholar
Matraji, I., Al-Durra, A., & Errouissi, R. (2018). Design and experimental validation of enhanced adaptive second-order SMC for PMSG-based wind energy conversion system. International Journal of Electrical Power & Energy Systems, 103, 21–30.
DOI: https://doi.org/10.1016/j.ijepes.2018.05.022
Google Scholar
Parsa, N.T., Vali, A., & Ghasemi, R. (2014). Back Stepping Sliding Mode Control of Blood Glucose for Type I Diabetes. World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 8(11), 779–783.
Google Scholar
Sylvester, D.D., & Munje, R.K. (2017). Back stepping SMC for blood glucose control of type-1 diabetes mellitus patients. International Journal of Engineering Technology Science and Research, 4(5), 1–7.
Google Scholar
Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft Computing, 22(2), 387–408.
DOI: https://doi.org/10.1007/s00500-016-2474-6
Google Scholar
Authors
Ekhlas H. KARAMek_karam@yahoo.com
* Mustansiriyah University, College of Engineering, Computer Engineering Department, Palestine Street, 14022, Baghdad Iraq
Authors
Eman H. JADOOMustansiriyah University, College of Engineering, Computer Engineering Department, Palestine Street, 14022, Baghdad Iraq
Statistics
Abstract views: 147PDF downloads: 28
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Maria TOMASIKOVA, Frantisek BRUMERČÍK, Aleksander NIEOCZYM, DESIGN AND DYNAMICS MODELING FOR ELECTRIC VEHICLE , Applied Computer Science: Vol. 13 No. 3 (2017)
- Lucian LUPŞA-TĂTARU, IMPLEMENTING THE FADE-IN AUDIO EFFECT FOR REAL-TIME COMPUTING , Applied Computer Science: Vol. 15 No. 2 (2019)
- Robert KARPIŃSKI, Jakub GAJEWSKI, Jakub SZABELSKI, Dalibor BARTA, APPLICATION OF NEURAL NETWORKS IN PREDICTION OF TENSILE STRENGTH OF ABSORBABLE SUTURES , Applied Computer Science: Vol. 13 No. 4 (2017)
- Puppala Praneeth, Majety Sathvika, Vivek Kommareddy, Madala Sarath, Saran Mallela, Koneru Suvarna Vani, Prasun Chkrabarti, CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK , Applied Computer Science: Vol. 19 No. 2 (2023)
- Mohammed A. Hussein, Ekhlas H. Karam, Rokaia S. Habeeb, CANCER GROWTH TREATMENT USING IMMUNE LINEAR QUADRATIC REGULATOR BASED ON CROW SEARCH OPTIMIZATION ALGORITHM , Applied Computer Science: Vol. 17 No. 2 (2021)
- Waldemar SUSZYŃSKI, Małgorzata CHARYTANOWICZ, Wojciech ROSA, Leopold KOCZAN, Rafał STĘGIERSKI, DETECTION OF FILLERS IN THE SPEECH BY PEOPLE WHO STUTTER , Applied Computer Science: Vol. 17 No. 4 (2021)
- Lucian LUPŞA-TĂTARU, NOVEL TECHNIQUE OF CUSTOMIZING THE AUDIO FADE-OUT SHAPE , Applied Computer Science: Vol. 14 No. 3 (2018)
- Wieslaw FRĄCZ, Grzegorz JANOWSKI, INFLUENCE OF HOMOGENIZATION METHODS IN PREDICTION OF STRENGTH PROPERTIES FOR WPC COMPOSITES , Applied Computer Science: Vol. 13 No. 3 (2017)
- Piotr Miś, Przemysław Szulim, ANALYSIS OF THE POSSIBILITY OF USING MARKERS EMITTING PULSATING LIGHT IN THE TASK OF LOCALIZATION , Applied Computer Science: Vol. 17 No. 1 (2021)
- Grzegorz RADZKI, Amila THIBBOTUWAWA, Grzegorz BOCEWICZ, UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH , Applied Computer Science: Vol. 15 No. 3 (2019)
You may also start an advanced similarity search for this article.