An efficient nonlinear explicit model predictive control to regulate blood glucose in type-1 diabetic patient under parametric uncertainties

Acharya, Debasis; Das, Dushmanta Kumar

doi:10.1016/j.bspc.2021.103166

Cited by 12 publications

(8 citation statements)

References 36 publications

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“…Remark 3. The possibility of external access to u x and/or u y is of great importance when the problem of artificially controlling a physiological system is faced [43,44], which is not a goal of this paper and will be discussed in future work; in those cases, the external contribution to the production rates can render u x and u y varying in time.…”

Section: Reference Model For a Physiological Control Systemmentioning

confidence: 99%

A General Approach for the Modelling of Negative Feedback Physiological Control Systems

et al. 2023

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Mathematical models can improve the understanding of physiological systems behaviour, which is a fundamental topic in the bioengineering field. Having a reliable model enables researchers to carry out in silico experiments, which require less time and resources compared to their in vivo and in vitro counterparts. This work’s objective is to capture the characteristics that a nonlinear dynamical mathematical model should exhibit, in order to describe physiological control systems at different scales. The similarities among various negative feedback physiological systems have been investigated and a unique general framework to describe them has been proposed. Within such a framework, both the existence and stability of equilibrium points are investigated. The model here introduced is based on a closed-loop topology, on which the homeostatic process is based. Finally, to validate the model, three paradigmatic examples of physiological control systems are illustrated and discussed: the ultrasensitivity mechanism for achieving homeostasis in biomolecular circuits, the blood glucose regulation, and the neuromuscular reflex arc (also referred to as muscle stretch reflex). The results show that, by a suitable choice of the modelling functions, the dynamic evolution of the systems under study can be described through the proposed general nonlinear model. Furthermore, the analysis of the equilibrium points and dynamics of the above-mentioned systems are consistent with the literature.

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Section: Reference Model For a Physiological Control Systemmentioning

confidence: 99%

A General Approach for the Modelling of Negative Feedback Physiological Control Systems

et al. 2023

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“…They have achieved better prediction accuracy, even though, their model failed to predict the severity level of the disease. Acharya et al [22] developed a nonlinear disease prediction model that considers the blood glucose and predict the type-1 diabetes. They have predicted the uncertainty of the disease levels effectively.…”

Section: Literature Surveymentioning

confidence: 99%

An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

Talari¹,

Suresh²,

Kavitha³

2023

Intelligent Automation &Amp; Soft Computing

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As per World Health Organization report which was released in the year of 2019, Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world. Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it. Among the diabetics, it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2. To avoid this situation, we propose a new fuzzy logic based neural classifier for early detection of diabetes. A set of new neuro-fuzzy rules is introduced with time constraints that are applied for the first level classification. These levels are further refined by using the Fuzzy Cognitive Maps (FCM) with time intervals for making the final decision over the classification process. The main objective of this proposed model is to detect the diabetes level based on the time. Also, the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction. The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.

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“…In comparison with other models, the main advantage of the Bergman minimal model is its simplicity, where the relation of input and output is regulated with the minimum possible parameters, without further involvement of biological complexity. The dynamic equations of the system are as follows 35 – 38 : where is the glucose concentration in the blood plasma in , is the interstitial insulin in and is the insulin concentration in the blood plasma in (or ), and are the basal levels of glucose and insulin respectively, is the time constant for insulin disappearance, , and are the insulin-independent constant rate of glucose uptake in muscles and liver, the rate for the decrease in tissue glucose uptake ability, and the insulin-dependent increase in glucose uptake ability in tissue per unit of insulin concentration above the basal level. The control input in denotes the insulin injection rate, and shows the glucose taken from meals which are uncertain in measure as a disturbance.…”

Section: Mathematical Model Of Type-1 Diabetesmentioning

confidence: 99%

Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm

Zahedifar

Khalaji

2022

Sci Rep

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In this study, an adaptive backstepping method is proposed to regulate the blood glucose induced by meals for type-1 diabetic patients. The backstepping controller is used to control the blood glucose level and an adaptive algorithm is utilized to compensate for the blood glucose induced by meals. Moreover, the effectiveness of the proposed method is evaluated by comparing results in two different case studies: in the presence of actuator faults and the loss of control input for a short while during treatment. Effects of unannounced meals three times a day are investigated for a nominal patient in every case. It is argued that adaptive backstepping is the preferred control method in either case. The Lyapunov theory is used to prove the stability of the proposed method. Obtained results, indicated that the adaptive backstepping controller is stable, and the desired level of glucose concentration is being tracked efficiently.

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An efficient nonlinear explicit model predictive control to regulate blood glucose in type-1 diabetic patient under parametric uncertainties

Cited by 12 publications

References 36 publications

A General Approach for the Modelling of Negative Feedback Physiological Control Systems

A General Approach for the Modelling of Negative Feedback Physiological Control Systems

An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm

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