Analysis of the performances of various controllers adopted in the biomedical field for blood glucose regulation: a case study of the type-1 diabetes

References

• Uhlemeyer C, Müller N, Rieck M, et al. Selective ablation of P53 in pancreatic beta cells fails to ameliorate glucose metabolism in genetic, dietary and pharmacological models of diabetes mellitus. Mol Metab. 2023;67:101650. doi: 10.1016/j.molmet.2022.101650.
   PubMed Web of Science ®Google Scholar
• Farman M, Saleem MU, Ahmad A, et al. A control of glucose level in insulin therapies for the development of ­artificial pancreas by Atangana Baleanu derivative. Alex Eng J. 2020;59(4):2639–2648. doi: 10.1016/j.aej.2020.04.027.
   Web of Science ®Google Scholar
• Farman M, Ahmad A, Zehra A, et al. Analysis and controllability of diabetes model for experimental data by using fractional operator. Math Comput Simul. 2024;218:133–148. doi: 10.1016/j.matcom.2023.11.017.
   Web of Science ®Google Scholar
• Farman M, Batool M, Nisar KS, et al. Controllability and analysis of sustainable approach for cancer treatment with chemotherapy by using the fractional operator. Results Phys. 2023;51:106630. doi: 10.1016/j.rinp.2023.106630.
   Web of Science ®Google Scholar
• Tanbek K, Yılmaz U, Gul M, et al. Effects of central FGF21 infusion on the glucose homeostasis in rats (brain–pancreas axis). Arch Physiol Biochem. 2023:1–8. doi: 10.1080/13813455.2023.2166964.
   PubMed Web of Science ®Google Scholar
• Zhang Y-B, Pan X-F, Lu Q, et al. Association of combined healthy lifestyles with cardiovascular disease and mortality of patients with diabetes: an international multicohort study. Mayo Clin Proc. 2023;98(1):60–74. doi: 10.1016/j.mayocp.2022.08.012.
   PubMed Web of Science ®Google Scholar
• Patra AK, Panigrahi GS, Nanda A. An automatic artificial pancreas based on AOA-VPTIDF control algorithm. Int J Adv Mech Syst. 2023;10(1):21–32. doi: 10.1504/IJAMECHS.2023.128159.
   Google Scholar
• Ndakara AI, Essabbar M, Saikouk H. Blood glucose-insulin dynamics in type-1 diabetic patients for the mitigation of hyperglycemic conditions: a PID controller with a step response. In: Digital Technologies and Applications: Proceedings of ICDTA'23, Fez, Morocco, Volume 1. Springer; 2023. p. 949–956.
   Google Scholar
• Omar AS, Faiz KA, Saeed KMI, et al. Epidemiologic and clinical characteristics of diabetic foot ulcer among patients with diabetes in Afghanistan: an IDF supported initiative. Diabetes Res Clin Pract. 2023;196:110227. doi: 10.1016/j.diabres.2022.110227.
   PubMed Web of Science ®Google Scholar
• Huang Y-K, Chang Y-C. Oral health: the first step to ­sustainable development goal 3. J Formos Med Assoc. 2022;121(7):1348–1350. doi: 10.1016/j.jfma.2021.10.018.
   PubMed Web of Science ®Google Scholar
• Dalenberg JR, Patel BP, Denis R, et al. Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. Cell Metab. 2020;31(3):493–502.e7. doi: 10.1016/j.cmet.2020.01.014.
   PubMed Web of Science ®Google Scholar
• Liu T, Gu Y, Waleed ALA, et al. Challenges and opportunities in developing low glycemic index foods with white kidney bean α-amylase inhibitor. Trends Food Sci Technol. 2024;147:104397. doi: 10.1016/j.tifs.2024.104397.
   Web of Science ®Google Scholar
• Maity D, Ray PG, Buchmann P, et al. Blood-glucose-powered metabolic fuel cell for self-sufficient bioelectronics. Adv Mater. 2023;35(21):2300890. doi: 10.1002/adma.202300890.
   Web of Science ®Google Scholar
• Batmani Y, Khodakaramzadeh S, Moradi P. Automatic artificial pancreas systems using an intelligent multiple-model PID strategy. IEEE J Biomed Health Inform. 2022;26(4):1708–1717. doi: 10.1109/JBHI.2021.3116376.
   PubMed Web of Science ®Google Scholar
• Soon GS, Torbenson M. The liver and glycogen: in sickness and in health. Int J Mol Sci. 2023;24(7):6133. doi: 10.3390/ijms24076133.
   PubMed Web of Science ®Google Scholar
• Kawamori D, Sasaki S. Newly discovered knowledge pertaining to glucagon and its clinical applications. J Diabetes Investig. 2023;14(7):829–837. doi: 10.1111/jdi.14009.
   PubMed Web of Science ®Google Scholar
• Gabbay MAL, Rodacki M, Calliari LE, et al. Time in range: a new parameter to evaluate blood glucose control in patients with diabetes. Diabetol Metab Syndr. 2020;12(1):22. doi: 10.1186/s13098-020-00529-z.
   PubMed Web of Science ®Google Scholar
• Ducluzeau PH, Fauchier G, Herbert J, et al. Prevalence and incidence of cardiovascular and renal diseases in type 1 compared with type 2 diabetes: a nationwide French observational study of hospitalized patients. Diabetes Metab. 2023;49(3):101429. doi: 10.1016/j.diabet.2023.101429.
   PubMed Web of Science ®Google Scholar
• Sangtani A, Owens L, Broome DT, et al. The impact of new and renewed restrictive state abortion laws on pregnancy-capable people with diabetes. Curr Diab Rep. 2023;23(8):175–184. doi: 10.1007/s11892-023-01512-4.
   PubMed Web of Science ®Google Scholar
• Batmani Y. Blood glucose concentration control for type 1 diabetic patients: a non-linear suboptimal approach. IET Syst Biol. 2017;11(4):119–125. doi: 10.1049/iet-syb.2016.0044.
   PubMed Web of Science ®Google Scholar
• Domingo-Lopez DA, Lattanzi G, H J Schreiber L, et al. Medical devices, smart drug delivery, wearables and technology for the treatment of diabetes mellitus. Adv Drug Deliv Rev. 2022;185:114280. doi: 10.1016/j.addr.2022.114280.
   PubMed Web of Science ®Google Scholar
• Tauschmann M, Hovorka R. Technology in the management of type 1 diabetes mellitus—current status and future prospects. Nat Rev Endocrinol. 2018;14(8):464–475. doi: 10.1038/s41574-018-0044-y.
   PubMed Web of Science ®Google Scholar
• Mirzaee A, Dehghani M, Mohammadi M. Robust LPV control design for blood glucose regulation considering daily life factors. Biomed Signal Process Control. 2020;57:101830. doi: 10.1016/j.bspc.2019.101830.
   Web of Science ®Google Scholar
• Mughal IS, Patanè L, Caponetto R. A comprehensive review of models and nonlinear control strategies for blood glucose regulation in artificial pancreas. Annu Rev Control. 2024;57:100937. doi: 10.1016/j.arcontrol.2024.100937.
   Web of Science ®Google Scholar
• Patra AK, Mishra AK, Rout PK. Backstepping model predictive controller for blood glucose regulation in type-I diabetes patient. IETE J Res. 2020;66(3):326–340. doi: 10.1080/03772063.2018.1493404.
   Web of Science ®Google Scholar
• Ming W, Guo X, Zhang G, et al. Recent advances in the precision control strategy of artificial pancreas. Med Biol Eng Comput. 2024;62(6):1615–1638.
   PubMed Web of Science ®Google Scholar
• Nath A, Biradar S, Balan A, et al. Physiological models and control for type 1 diabetes mellitus: a brief review. IFAC-PapersOnLine. 2018;51(1):289–294. doi: 10.1016/j.ifacol.2018.05.077.
   Google Scholar
• Khodakaramzadeh S, Batmani Y, Meskin N. Automatic blood glucose control for type 1 diabetes: a trade-off between postprandial hyperglycemia and hypoglycemia. Biomed Signal Process Control. 2019;54:101603. doi: 10.1016/j.bspc.2019.101603.
   Web of Science ®Google Scholar
• Batmani Y, Khodakaramzadeh S. Blood glucose concentration control for type 1 diabetic patients: a multiple-model strategy. IET Syst Biol. 2020;14(1):24–30. doi: 10.1049/iet-syb.2018.5049.
   PubMed Web of Science ®Google Scholar
• Bergman RN, Prager R, Volund A, et al. Equivalence of the insulin sensitivity index in man derived by the minimal model method and the euglycemic glucose clamp. J Clin Invest. 1987;79(3):790–800. doi: 10.1172/JCI112886.
   PubMed Web of Science ®Google Scholar
• Dalla Man C, Rizza RA, Cobelli C. Meal simulation model of the glucose-insulin system. IEEE Trans Biomed Eng. 2007;54(10):1740–1749. doi: 10.1109/TBME.2007.893506.
   PubMed Web of Science ®Google Scholar
• Hu M, Suthers PF, Maranas CD. KETCHUP: parameterizing of large-scale kinetic models using multiple datasets with different reference states. Metab Eng. 2024;82:123–133. doi: 10.1016/j.ymben.2024.02.002.
   PubMed Web of Science ®Google Scholar
• Syafiie S, Alharbi F, Alshehri AA, et al. A new approach to stabilize diabetes systems with time-varying delays and disturbance rejection. J Franklin Inst. 2024;361(1):543–556. doi: 10.1016/j.jfranklin.2023.12.012.
   Web of Science ®Google Scholar
• Jacknoon A, Hassan M, Ferik SE. Design of RST controllers based on intelligent optimization algorithms. In: 2016 Conference of Basic Sciences and Engineering Studies (SGCAC). IEEE; 2016. p. 177–182. doi: 10.1109/SGCAC.2016.7458025.
   Google Scholar
• İçmez Y, Can MS. Smith predictor controller design using the direct synthesis method for unstable second-Order and time-delay systems. Processes. 2023;11(3):941. doi: 10.3390/pr11030941.
   Web of Science ®Google Scholar
• Williams RL, Lawrence DA. Linear state-space control systems. Nashville (TN): John Wiley & Sons; 2007.
   Google Scholar
• Plant-level control and protection modeling task force. [Online]. Available from: https://www.nerc.com/comm/PC/PCPMTF_DL/Final_PCPMTF-Report.pdf
   Google Scholar
• Maahs DM, Buckingham BA, Castle JR, et al. Outcome measures for artificial pancreas clinical trials: a consensus report. Diabetes Care. 2016;39(7):1175–1179. doi: 10.2337/dc15-2716.
   PubMed Web of Science ®Google Scholar