A robust sliding mode controller with internal model for closed-loop artificial pancreas

Abu-Rmileh, Amjad; Garcia-Gabin, Winston; Zambrano, Darine

doi:10.1007/s11517-010-0665-3

Cited by 25 publications

(7 citation statements)

References 31 publications

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“…This is a second-order model for the glucose–insulin relationship and first-order for the glucose–meal relationship. 16 The model is fitted to the average patient response of the University of Virginia (UVa)/Padova simulator. 12…”

Section: Methodsmentioning

confidence: 99%

“…This is a second-order model for the glucose-insulin relationship and first-order for the glucose-meal relationship. 16 The model is fitted to the average patient response of the University of Virginia (UVa)/Padova simulator. 12 The tuning parameters for both the low-order and the multilevel MPC are N 1 = 1, N p = 85, N u = 2, and d Δu = 1.5.…”

Section: Model Predictive Control Based On Multilevel Modelmentioning

confidence: 99%

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Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

Garcia-Gabin

Jacobsen

2013

J Diabetes Sci Technol

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Multilevel models open new possibilities for designing glucose control algorithms. They allow controllers to take into account variables that have a strong influence on glucose homeostasis. A model-based controller was used for demonstrating how improved knowledge of the multilevel nature of diabetes increases the robustness and performance of glucose control algorithms. Using the proposed multi-level approach, a reduction of the hypoglycemic risk and robust behaviour for intrapatient variability was demonstrated.

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Section: Methodsmentioning

confidence: 99%

Section: Model Predictive Control Based On Multilevel Modelmentioning

confidence: 99%

Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

Garcia-Gabin

Jacobsen

2013

J Diabetes Sci Technol

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“…Concerning applicability, the mechanistic model derived here could be integrated into the in silico metabolic simulation of meals [1,6,11,19,24,30], in order to optimize postprandial insulin infusion regimes. In addition, those simulations are essential as well for artificial pancreas systems in T1D [17], specially for those based on model predictive control algorithms [13] and therefore relying on physiologically sound models.…”

Section: Model Identification Validation and Selectionmentioning

confidence: 99%

“…Notably, a work by Ellen et al [9] studied for the first time meal absorption in TID patients who had consumed common mixed meals with complex carbohydrates (CHO), as their dynamics may differ from simpler forms of glucose. The aim of that study was twofold: (1) to obtain mechanistic information applicable to postprandial glycaemia management and (2) to contribute to the field of overnight closed-loop control in TID. In brief, authors reported that the absorption rate for meal-related glucose CR a ,meai) reached its maximum at higher values and later times for meals with high-glycaemic-load (HG) than for low-glycaemic-load (LG) meals.…”

Section: Introductionmentioning

confidence: 99%

Modelling the effect of insulin on the disposal of meal-attributable glucose in type 1 diabetes

García‐García

Hovorka

Wilinska

et al. 2016

Med Biol Eng Comput

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The management of postprandial glucose excursions in type 1 diabetes has a major impact on overall glycaemic control. In this work, we propose and evaluate various mechanistic models to characterize the disposal of meal-attributable glucose. Sixteen young volunteers with type 1 diabetes were subject to a variable-target clamp which replicated glucose profiles observed after a highglycaemic-load (n = 8) or a low-glycaemic-load (n = 8) evening meal. [6,6-2 H 2 ] and [U-13 C;1,2,3,4,5,6,6-2 H 7 ] glucose tracers were infused to, respectively, mimic: (a) the expected post-meal suppression of endogenous glucose production and (b) the appearance of glucose due to a standard meal. Six compartmental models (all a priori identifiable) were proposed to investigate the remote effect of circulating plasma insulin on the disposal of those glucose tracers from the non-accessible compartments, representing e.g. interstitium. An iterative population-based parameter fitting was employed. Models were evaluated attending to physiological plausibility, posterior identifiability of their parameter estimates, accuracy-via weighted fitting residuals-and information criteria (i.e. parsimony). The most plausible model, best representing our experimental data, comprised: (1) a remote effect x of insulin active above a threshold x c = 1.74 (0.81-2.50) • 10~2 min -1 [median (inter-quartile range)], with parameter XQ having a satisfactory support: coefficient of variation CV = 42.33 (31.34-65.34) %, and (2) steady-state conditions at the onset of the experiment (t = 0) for the compartment representing the remote effect, but not for the masses of the tracer that mimicked endogenous glucose production. Consequently, our mechanistic model suggests non-homogeneous changes in the disposal rates for meal-attributable glucose in relation to plasma insulin. The model can be applied to the in silico simulation of meals for the optimization of postprandial insulin infusion regimes in type 1 diabetes.

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“…A significant effort has been put forth towards the development of a closed-loop algorithm for blood glucose control [2][3][4][5][6][7]. These approaches have utilized exclusively feedback control to maintain normoglycemia, even in a case of disturbance occurrence.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of blood sugar in patients with type‐1 diabetes

Najafabadi

Shahrokhi

2015

Optim Control Appl Methods

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Summary In this article, two adaptive model predictive controllers (AMPC) are applied to regulate the blood glucose in type 1 diabetic patients. The first controller is constructed based on a linear model, while the second one is designed by using a nonlinear Hammerstein model. The adaptive version of these control schemes is considered to make them more robust against model mismatches and external disturbances. The least squares method with forgetting factor is used to update the model parameters. For simulation study, two well‐known mathematical models namely, Puckett and Hovorka which describe the dynamical behavior of patient's body have been selected. The performances and robustness of the proposed controllers are tested for regulating the blood glucose of diabetic patients in presences of model mismatches and measurement noises. Simulation results indicate that the non‐linear model predictive controller (NMPC) outperforms the linear one. To improve the performance of the NMPC in rejecting the meal disturbances, two different feedforward control strategies have been considered. Simulation results indicate that the combined adaptive NMPC with feedforward controller has a better performance over the other considered control schemes. Copyright © 2015 John Wiley & Sons, Ltd.

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A robust sliding mode controller with internal model for closed-loop artificial pancreas

Cited by 25 publications

References 31 publications

Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

Multilevel Model of Type 1 Diabetes Mellitus Patients for Model-Based Glucose Controllers

Modelling the effect of insulin on the disposal of meal-attributable glucose in type 1 diabetes

Model predictive control of blood sugar in patients with type‐1 diabetes

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