A T-S Fuzzy Model-Based Algorithm for Blood Glucose Control in Diabetic Patients

Kang, Zigui; Pan, Juntao; Zhang, Weiwei; Zou, Xiaoyan

doi:10.1109/iaeac.2018.8577639

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Many research options are under developments in which Bi-hormonal artificial pancreas using switching Economic predictive control model which consist of insulin subsystem and glucose subsystem can be predictively controlled by switching PID controller. Another model which is based on a nonlinear control system based on (TS) Fuzzy model which consists of an effective and exact representation of a complex nonlinear system with a compact set of variable [18][19][20][21][22][23][24].By the above methods successful implementation of artificial pancreas or insulin pumps depends upon exact calculations of activity monitoring, measurement of glucose, carbohydrate intake in the body. Basic structure of artificial pancreas will be explained by Figure no.6.…”

Section: E Artificial Pancreasmentioning

confidence: 99%

“…With this strong relationship between insulin and glucose contents. We can develop a mathematical model with the help of artificial intelligence or closed loop control system with the help of switching PID predictive control [18][19][20][21][22][23][24]. Also, we can make model of nonlinear differential equations to develop artificial pancreas.…”

Section: Introductionmentioning

confidence: 99%

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Refining Healthcare in Terms of Diabetic Care: Future Area of Scope for Artificial Intelligence

2019

IJRTE

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Recently, the entire world is facing another noncommunicable disease known as Diabetics. Basic reasons behind this disease is not unique , but certainly may be due to an increase in economic burden, less activity, change in lifestyle, improper food intake and increase in the level of stress. This disease has its effects all over the ages in the world from child to old age people. Hence its treatment is a big challenge to the social and economic growth of every nation. Plenty of research is undergoing in this area to improve health care which requires certain modifications and validations by clinical trials. Advancement in artificial intelligence is able to provide unique solutions in the future. The basic motivation behind this paper is to study the entire innovation taking place in recent years in the area of diabetic prevention and detection. Continuous monitoring of blood glucose level by invasive and non-invasive sensor technology, diet monitoring system, activity monitoring, intelligent foot and artificial pancreas for diabetics is the major focus of this study. Also, these studies focus on complete medical healthcare solution by using artificial intelligence. This study will surely help to identify future scope in the area of diabetic and its prevention models which can act as a future of diabetic health care

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Section: E Artificial Pancreasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Refining Healthcare in Terms of Diabetic Care: Future Area of Scope for Artificial Intelligence

2019

IJRTE

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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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Robust dynamic output feedback control of blood glucose level in diabetic rat with robust descriptor Kalman filter

Rahmanian

Asemani

Dehghani

et al. 2022

Biomedical Signal Processing and Control

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A T-S Fuzzy Model-Based Algorithm for Blood Glucose Control in Diabetic Patients

Cited by 3 publications

References 9 publications

Refining Healthcare in Terms of Diabetic Care: Future Area of Scope for Artificial Intelligence

Refining Healthcare in Terms of Diabetic Care: Future Area of Scope for Artificial Intelligence

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

Robust dynamic output feedback control of blood glucose level in diabetic rat with robust descriptor Kalman filter

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