Comparison of PID based Control Algorithms for Daily Blood Glucose Control

Soylu, Selim; Danışman, Kenan

doi:10.11159/eee16.130

Cited by 8 publications

(6 citation statements)

References 22 publications

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“…There are many previous studies that have dealt with this topic using the Bergman model, controlling it and dealing with it from different angles, including the use of the classical controller (PID) provided by Ramprasad, Y, Rangaiah, G in 2004 and others for example [10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

Benzian¹,

Ameur²,

Rebai³

2021

JESA

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Diabetes is one of the most important diseases that researchers have focused on in scientific research since the time, because of the seriousness of this disease if it is not properly dealt with, especially with the emergence of some global epidemics such as Corona Virus (COVID 19), as the pancreas is the organ responsible for regulating sugar in the blood by secreting the insulin enzyme, insulin is widely used to control blood sugar. Therefore, it is important that the required insulin value is constant and controlled. The aim of this study is to control the blood glucose value that is achieved as a desired value and to maintain it as a constant value using a proportional, integral, and derivative control unit (FOPID) fractional order of the control parameters. In this research, the new control unit is applied to Bergman's mathematical model as a non-linear and simple model that simulates the mechanism of the interaction of glucose and insulin in the blood, and based on this, a closed control loop was designed to regulate the level of blood sugar to be an automatic control of blood glucose using the measured data from Special sensor. The contribution in this scientific paper is to define the (FOPID) parameters according to the closed loop responses of the system, and these parameters were adjusted using new meta-heuristic algorithms including the Invasive Weed Optimization (IWO), the PSO Particle Swarm optimization, the Genetic Algorithm (GA), The bat optimization algorithm (BA) and (ACO). As a result, the results of the five modern algorithms were compared based on several criteria to find out which one was better using MATLAB / SIMULINK simulation. It was found that the IWO algorithm performs better than PSO. The simulation results of the closed-loop system of this controller at the time of settling, overshoot and control inputs indicate very positive results compared to previous results. In addition, a new method has been proposed which is to design a pump in the form of a valve to control insulin pumping by controlling it with the fuzzy logic control unit, which in turn, we obtained better results, compared to the results of other previous studies.

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Section: Introductionmentioning

confidence: 99%

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

Benzian¹,

Ameur²,

Rebai³

2021

JESA

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“…Non-model based open loop algorithms such as Pankowska and Food Insulin Index are also available and are not widely used because they cause too many hypoglycemia events. Model-based algorithms could not be presented to the practical use of all patients and physicians, due to their complex mathematical structures and person-based parameters [21,22].…”

Section: Introductionmentioning

confidence: 99%

Three Parameter Control Algorithm for Obtaining Ideal Postprandial Blood Glucose in Type 1 Diabetes Mellitus

Çankaya

Aydoğdu

2020

IEEE Access

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The three main parameters used by patients in diabetes management are initial blood glucose, carbohydrate amount, and mealtime difference. In this study, a novel open-loop control algorithm was proposed, which aims to determine the correct value of mealtime difference duration. Unlike current blood glucose estimation methods, this algorithm was focused on determining extreme points that only concern patients such as hyperglycemia and hypoglycemia, instead of predicting all blood glucose trace in the postprandial period. This new approach has made it possible to determine extreme blood glucose values with simple linear equations without using complex models. This algorithm has only three parameters and has been validated in the UVA Padova type 1 diabetes mellitus simulator, which has thirty in-silico patients. The regressions between real and predicted values for hypoglycemia, hyperglycemia, and mean blood glucose were 0.95, 0.99, and 0.98, respectively. Using the proposed algorithm, the severity of hyperglycemia, mean blood glucose, and standard deviation values were reduced, and hypoglycemia events were prevented. Postprandial blood glucose curves have occurred as desired and within normal limits. Furthermore, it was concluded that intersection points of blood glucose curves contain information about the metabolic parameters of the patients, in this study.

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“…Especially for patients with T1DM, exogenous insulin should be infused at an appropriate rate in order to keep blood glucose concentration (BGC) in a normoglycemic range (70-180 mg/dL [2]). To infuse exogenous insulin at an appropriate rate, a closed-loop control system has been widely used [2][3][4], as illustrated in Figure 1. The closed-loop control system is also known as the artificial pancreas, which consists of a glucose sensor or continuous glucose monitor (CGM), a controller, and an insulin pump.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Thanks to advances in modeling, control theory, and biomedical engineering, several control algorithms have been proposed to keep the BGC of the patients with T1DM in the normoglycemic range. These algorithms are mainly based on classical controllers [3,8,9], adaptive controllers [10], or robust controllers [4]. Although these controllers have a good performance in simulation studies, they are unable to cope with the uncertainty and nonlinearity of biological systems, such as that of the glucose-insulin regulatory system [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Blood glucose control using an ABC algorithm-based fuzzy-PID controller

Soylu¹,

Danışman²

2018

Turk J Elec Eng & Comp Sci

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In this paper, a Mamdani-type fuzzy controller is proposed as the controller part of an artificial pancreas. The controller is optimized with the artificial bee colony optimization algorithm. The glucose-insulin regulatory system, based on a nonlinear differential model in the presence of delay, is used both for virtual patient and healthy person data. The main target of the controller is to mimic a blood glucose concentration profile of the healthy person with exogenous insulin infusion. Simulations are performed to assess the control function in terms of tracking the blood glucose concentration profile of the healthy person and minimizing errors. To show robustness, a group of three tests are implemented. These tests include unusual glucose intake, sensor noise, and uncertainty in the clearance rate parameter. The simulation results demonstrate that the adopted method is more effective than similar studies in the literature.

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Comparison of PID based Control Algorithms for Daily Blood Glucose Control

Cited by 8 publications

References 22 publications

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

Three Parameter Control Algorithm for Obtaining Ideal Postprandial Blood Glucose in Type 1 Diabetes Mellitus

Blood glucose control using an ABC algorithm-based fuzzy-PID controller

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