Modelling and Simulation Study of Glucose Insulin Control in Type 1 Diabetic Patient Used for Developing Artificial Pancreas

Dias, Cifha Crecil; Kamath, Surekha; Vidyasagar, Sudha

doi:10.1109/aicai.2019.8701401

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Sorensen model is a human glucose–insulin glucagon regulatory interaction model. The complex interaction of this model is captured and is delivered in terms of mathematical differential ordinary equations [40, 41 ]. The captured dynamics of the Sorensen model is non‐linear model equations, which is linearised with an operating point.…”

Section: Methodsmentioning

confidence: 99%

Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm

Dias

Kamath

Vidyasagar

2020

IET Systems Biology

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The complete automated control and delivery of insulin and glucagon in type 1 diabetes is the developing technology for artificial pancreas. This improves the quality of life of a diabetic patient with the precise infusion. The amount of infusion of these hormones is controlled using a control algorithm, which has the prediction property. The control algorithm model predictive control (MPC) predicts one step ahead and infuses the hormones continuously according to the necessity for the regulation of blood glucose. In this research, the authors propose a MPC control algorithm, which is novel for a dual hormone infusion, for a mathematical model such as Sorenson model, and compare it with the insulin alone or single hormone infusion developed with MPC. Since they aim for complete automatic control and regulation, unmeasured disturbances at a random time are integrated and the performance evaluation is projected through statistical analysis. The blood glucose risk index (BGRI) and control variability grid analysis (CVGA) plot gives the additional evaluation for the comparative results of the two controllers claiming 88% performance by dual hormone evaluated through CVGA plot and 2.05 mg/dl average tracking error, 2.20 BGRI. The MPC developed for dual hormone significantly performs better and the time spent in normal glycaemia is longer while eliminating the risk of hyperglycaemia and hypoglycaemia.

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Section: Methodsmentioning

confidence: 99%

Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm

Dias

Kamath

Vidyasagar

2020

IET Systems Biology

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“…In this section, the model used only to describe the dynamic relationship between insulin, glucose, and glucagon. Thus, this model treats meals as unmeasured, unmodelled disturbances [37]. Here, the state at a certain time

t_{k + 1}

is calculated from the state and the insulin infusion rate of the previous time

t_{k}

.…”

Section: Problem Formulationmentioning

confidence: 99%

“…This model has been developed with mass balance equations of the blood flow, the exchange between the compartmental models and the metabolic process. The Food and Drug Administration approved model such as Cobelli was a widely used patient model but failed due to the inability of varying model parameters during the simulation [ 36 , 37 ]. Hovorka model with the six states glucose–insulin dynamics is the simplest non‐linear model, which can be used as patient model [ 38 , 39 ].…”

Section: Background Studymentioning

confidence: 99%

Blood glucose regulation and control of insulin and glucagon infusion using single model predictive control for type 1 diabetes mellitus

2020

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This study elaborates on the design of artificial pancreas using model predictive control algorithm for a comprehensive physiological model such as the Sorensen model, which regulates the blood glucose and can have a longer control time in normal glycaemic region. The main objective of the proposed algorithm is to eliminate the risk of hyper and hypoglycaemia and have a precise infusion of hormones: insulin and glucagon. A single model predictive controller is developed to control the bihormones, insulin, and glucagon for such a development unmeasured disturbance is considered for a random time. The simulation result for the proposed algorithm performed good regulation lowering the hypoglycaemia risk and maintaining the glucose level within the normal glycaemic range. To validate the performance of the tracking of output and setpoint, average tracking error is used and 4.4 mg/dl results are obtained while compared with standard value (14.3 mg/dl).

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Design and Implementation of Heartbeat Rate and SpO2 Detector by Using IoT for Patients

Natarasan¹,

Sekar²

2020

2020 International Conference on Electronics and Sustainable Communication Systems (ICESC)

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Modelling and Simulation Study of Glucose Insulin Control in Type 1 Diabetic Patient Used for Developing Artificial Pancreas

Cited by 3 publications

References 12 publications

Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm

Design of dual hormone blood glucose therapy and comparison with single hormone using MPC algorithm

Blood glucose regulation and control of insulin and glucagon infusion using single model predictive control for type 1 diabetes mellitus

Design and Implementation of Heartbeat Rate and SpO2 Detector by Using IoT for Patients

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