Delay-Independent regulation of blood glucose for type-1 diabetes mellitus patients via an observer-based predictor feedback approach by considering quantization constraints

Golestani, Farzaneh; Tavazoei, Mohammad Saleh

doi:10.1016/j.ejcon.2021.11.002

Cited by 15 publications

(12 citation statements)

References 40 publications

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“…Theorem 1. Under Assumption 1, consider the eventtriggered strategy (7) and the uniform dynamic quantization function (6) with the zooming variable equation (8).…”

Section: Resultsmentioning

confidence: 99%

“…Stability analysis of time-delay systems is one of the main problems in control systems engineering. Sensing delay 1,2 and actuation delay [3][4][5][6][7][8][9] are typical forms of time delays which may be simultaneously exist in control system loops, 10,11 and up to now, various effective control methods have been proposed to guarantee the closedloop stability in the presence of such time delays. In a control system with sensing delay, the controller may only have past information about state variables of the plant (sensing delay) and in a control system with actuation delay, the updated control signal may never reach the plant promptly (actuation delay).…”

Section: Introductionmentioning

confidence: 99%

“…In a control system with sensing delay, the controller may only have past information about state variables of the plant (sensing delay) and in a control system with actuation delay, the updated control signal may never reach the plant promptly (actuation delay). One of the most common stabilization methods in this context is based on using the family of predictor feedback controllers, 3,5,8,9,[12][13][14][15][16] due to their ability in prediction of the state variables of dynamical systems. Due to the implementation difficulty of predictor feedback, 12,13 it has been simplified to the truncated predictor one.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Event-triggered delay-independent truncated predictor feedback stabilization of Lipschitz nonlinear input delay systems with state quantization

Golestani

Tavazoei

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article addresses the event-triggered control design for stabilization of Lipschitz nonlinear systems with time-varying input delay and state quantization constraints. The stabilization is done by an event-triggered delay-independent truncated predictor feedback controller using the information of the upper bound of time-varying input delay (and not its exact value). A sufficient condition on the value of this upper bound in order to guarantee the stability of the closed-loop system under the state quantization constraints is derived. Also, the avoidance of Zeno behavior is proved by finding a lower bound for each two successive events. Various numerical examples illustrate the effectiveness of the obtained results.

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“…Theorem 1. Under Assumption 1, consider the eventtriggered strategy (7) and the uniform dynamic quantization function (6) with the zooming variable equation (8).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Event-triggered delay-independent truncated predictor feedback stabilization of Lipschitz nonlinear input delay systems with state quantization

Golestani

Tavazoei

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Srivastava et al [ 50 ] have focused on a fractional-order model of diabetes and they considered model with its complications. For more knowledge about diabetes and related studies, see in [ 16 , 17 , 20 , 54 , 58 ]…”

Section: The Diabetes Mellitus Modelmentioning

confidence: 99%

A computational technique for the Caputo fractal-fractional diabetes mellitus model without genetic factors

Karaağaç

Owolabi

Pindza

2023

Int. J. Dynam. Control

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The concept of a Caputo fractal-fractional derivative is a new class of non-integer order derivative with a power-law kernel that has many applications in real-life scenarios. This new derivative is applied newly to model the dynamics of diabetes mellitus disease because the operator can be applied to formulate some models which describe the dynamics with memory effects. Diabetes mellitus as one of the leading diseases of our century is a type of disease that is widely observed worldwide and takes the first place in the evolution of many fatal diseases. Diabetes is tagged as a chronic, metabolic disease signalized by elevated levels of blood glucose (or blood sugar), which results over time in serious damage to the heart, blood vessels, eyes, kidneys, and nerves in the body. The present study is devoted to mathematical modeling and analysis of the diabetes mellitus model without genetic factors in the sense of fractional-fractal derivative. At first, the critical points of the diabetes mellitus model are investigated; then Picard’s theorem idea is applied to investigate the existence and uniqueness of the solutions of the model under the fractional-fractal operator. The resulting discretized system of fractal-fractional differential equations is integrated in time with the MATLAB inbuilt Ode45 and Ode15s packages. A step-by-step and easy-to-adapt MATLAB algorithm is also provided for scholars to reproduce. Simulation experiments that revealed the dynamic behavior of the model for different instances of fractal-fractional parameters in the sense of the Caputo operator are displayed in the table and figures. It was observed in the numerical experiments that a decrease in both fractal dimensions and leads to an increase in the number of people living with diabetes mellitus.

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“…The controller design depends on the information of the states obtained from a nonlinear observer. Golestani and Tavazoei (2022) presents a novel truncated predictor output feedback controller that works based on a delay-independent observer. The controller is applied to a nonlinear BMM with meal disturbance.…”

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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Delay-Independent regulation of blood glucose for type-1 diabetes mellitus patients via an observer-based predictor feedback approach by considering quantization constraints

Cited by 15 publications

References 40 publications

Event-triggered delay-independent truncated predictor feedback stabilization of Lipschitz nonlinear input delay systems with state quantization

Event-triggered delay-independent truncated predictor feedback stabilization of Lipschitz nonlinear input delay systems with state quantization

A computational technique for the Caputo fractal-fractional diabetes mellitus model without genetic factors

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

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