Insulin infusion rate control in type 1 diabetes patients using information-theoretic model predictive control

Birjandi, Sahar Zadeh; Sani, Seyed Kamal Hosseini; Pariz, Naser

doi:10.1016/j.bspc.2022.103635

Cited by 7 publications

(2 citation statements)

References 33 publications

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“…For mentioned control approaches in [ 13 , 23 – 26 ] it provides satisfactory performance in regulating BGL of T1DPs against parametric uncertainty and meal disturbance however it considered in the design the possibility of measuring all state variables which practically is not true for reasons of high cost and reliability. In [ 27 – 29 ] model predictive control (MPC) was utilized to regulate BGL of T1DP, it has the ability to deal with parametric uncertainty of glucose-insulin physiological system however the computational of iterative online optimization process is complex and its efficiency depends on how the accuracy of the predicted output is, in [ 30 ] nonlinear explicit MPC (NEMPC) was used to avoid the computational complexity of iterative online optimization, in [ 31 ] the output error state observer was used with MPC to correct the output variable prediction and hence improve the performance of the MPC, also in [ 32 ] extended kalman filter was used with NEMPC to estimate unavailable states for T1DP to improve the accuracy of the predicted BGL. To deal with unmeasured state variables, observer was used in conjunction with nonlinear adaptive controller in [ 33 ], with backstepping controller in [ 9 ], and with predictor feedback controller in [ 34 ] to estimate unmeasured state variables.…”

Section: Introductionmentioning

confidence: 99%

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

Elhoushy,

Zalam,

Sayed

et al. 2023

Biomed. Eng. Lett.

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Regulating blood glucose level (BGL) for type-1 diabetic patient (T1DP) accurately is very important issue, an uncontrolled BGL outside the standard safe range between 70 and 180 mg/dl results in dire consequences for health and can significantly increase the chance of death. So the purpose of this study is to design an optimized controller that infuses appropriate amounts of exogenous insulin into the blood stream of T1DP proportional to the amount of obtained glucose from food. The nonlinear extended Bergman minimal model is used to present glucose-insulin physiological system, an interval type-2 fuzzy logic controller (IT2FLC) is utilized to infuse the proper amount of exogenous insulin. Superiority of IT2FLC in minimizing the effect of uncertainties in the system depends primarily on the best choice of footprint of uncertainty (FOU) of IT2FLC. So a comparison includes four different optimization methods for tuning FOU including hybrid grey wolf optimizer-cuckoo search (GWOCS) and fuzzy logic controller (FLC) method is constructed to select the best controller approach. The effectiveness of the proposed controller was evaluated under six different scenarios of T1DP using Matlab/Simulink platform. A 24-h scenario close to real for 100 virtual T1DPs subjected to parametric uncertainty, uncertain meal disturbance and random initial condition showed that IT2FLC accurately regulate BGL for all T1DPs within the standard safe range. The results indicated that IT2FLC using GWOCS can prevent side effect of treatment with blood-sugar-lowering medication. Also stability analysis for the system indicated that the system operates within the stability region of nonlinear system.

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Section: Introductionmentioning

confidence: 99%

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

Elhoushy,

Zalam,

Sayed

et al. 2023

Biomed. Eng. Lett.

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“…Regulation of blood glucose levels in T1DM patients is a type of difficult control problem. Most of the techniques employ simple solutions from proportional-integral (PI) and proportional–integral–derivative (PID) controllers (Paiva et al, 2020) to fuzzy control (Kang et al, 2018), reinforcement learning (RL) (Lee et al, 2020), and recently, MPC (Birjandi et al, 2022) to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Insulin infusion rate control using information theoretic–based nonlinear model predictive control for type 1 diabetes patients

Birjandi

Sani

Pariz

2022

Transactions of the Institute of Measurement and Control

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It has been proven that model predictive control (MPC) is an efficient method for closed-loop insulin delivery in clinical studies. This paper aims to design an observer-based fractional-order nonlinear MPC for type 1 diabetes mellitus (T1DM) patients. It is assumed that the proposed model is nonlinear and contains parametric uncertainty. To estimate unknown states, optimal non-fragile H∞ observer is designed for Lipschitz nonlinear fractional-order systems including parametric uncertainty and the existence of input disturbance. The min–max optimization-based robust fractional model predictive control (RFMPC) has been presented in the following for insulin delivery. Since sensor noise of continuous monitoring of interstitial glucose concentration is considered non-Gaussian, the performance of the proposed controller is improved under non-Gaussian measurement noise by selecting a proper cost function based on generalized correntropy, and as a contrast, the performance of the mean square error (MSE)-based controller is simulated. According to the results, not only is the performance of the proposed controller better under non-Gaussian situations but also effectively reaches the set point in the case of disturbance and uncertainty and provides higher control accuracy and robustness compared with the MSE-based MPC.

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Mathematical modeling of gastrointestinal starch digestion-blood glucose-insulin interactions

Meráz

Vernon‐Carter

Bello‐Pérez

et al. 2022

Biomedical Signal Processing and Control

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Insulin infusion rate control in type 1 diabetes patients using information-theoretic model predictive control

Cited by 7 publications

References 33 publications

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach

Insulin infusion rate control using information theoretic–based nonlinear model predictive control for type 1 diabetes patients

Mathematical modeling of gastrointestinal starch digestion-blood glucose-insulin interactions

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