Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas

Moscoso-Vásquez, Marcela; Colmegna, Patricio; Rosales, Nicolás; Garelli, Fabricio; Peña, Ricardo

doi:10.1109/jbhi.2020.2969389

Cited by 18 publications

(12 citation statements)

References 54 publications

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“…Indeed, discrepancies of physiological parameters have been observed between individuals as inter-patient variability [12,30]. But also, a given individual could present day-to-day variation as intra-patient variability [20,48]. Therefore, parameters p 1 , p 2 , p 5 and n are modeled as uniform random numbers on a domain with ±20% variation from the nominal values following [49].…”

Section: Nonlinear Dynamical Model Of Type 1 Diabetesmentioning

confidence: 99%

PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

et al. 2021

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Closed forms of stabilizing sets are generally only available for linearized systems. An innovative numerical strategy to estimate stabilizing sets of PI or PID controllers tackling (uncertain) nonlinear systems is proposed. The stability of the closed-loop system is characterized by the sign of the largest Lyapunov exponent (LLE). In this framework, the bottleneck is the computational cost associated with the solution of the system, particularly including uncertainties. To overcome this issue, an adaptive surrogate algorithm, the Monte Carlo intersite Voronoi (MiVor) scheme, is adopted to pertinently explore the domain of the controller parameters and classify it into stable/unstable regions from a low number of nonlinear estimations. The result of the random analysis is a stochastic set providing probability information regarding the capabilities of PI or PID controllers to stabilize the nonlinear system and the risk of instabilities. The minimum of the LLE is proposed as tuning rule of the controller parameters. It is expected that using a tuning rule like this results in PID controllers producing the highest closed-loop convergence rate, thus being robust against model parametric uncertainties and capable of avoiding large fluctuating behavior. The capabilities of the innovative approach are demonstrated by estimating robust stabilizing sets for the blood glucose regulation problem in type 1 diabetes patients.

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Section: Nonlinear Dynamical Model Of Type 1 Diabetesmentioning

confidence: 99%

PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

et al. 2021

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“…Diabetes disease can be [5] insulin-or non-insulindependent diabetes mellitus, widely known as type-1 or 2 diabetes, respectively. For modeling and capturing the behavior of the glucose-insulin, different state-space representations are presented, such as UVA/Padova simulator [6], [7], Hovorka model [8], deep reinforcement learning model [9], 19-order Sorensen model [10], linear empirical dynamic model [11], Tolic model [12], and Bergman minimal model [13]. Moreover, different control techniques are suggested to regulate the BGC in type-1 diabetes [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Robust Polytopic-LPV Body-Weight-Dependent Control of Blood Glucose in Type-1 Diabetes

et al. 2021

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This paper presents a robust ∞ polytopic Linear Parameter Varying (LPV) controller to regulate the blood glucose level in type-1 diabetes patients. The suggested approach utilizes an observer-based controller and polytopic inexact gain-scheduling scheme to construct the stabilizing injecting insulin resilient against the external meal and snack disturbance in Silico. To assure the closed-loop stability and prescribed ∞ performance, a Quadratic Lyapunov Function (QLF) is used and the stabilization conditions are derived in terms of Linear Matrix Inequalities (LMIs). The conventional minimal Bergman model is improved by considering the effect of body-weight on the glucose-insulin phenomenon and the new body-weight functions are identified based on real data. Then, for the body-weight-dependent Bergman model, a polytopic-LPV model is obtained. Simulations show the advantages of the developed glucose-insulin model and the robustness of the suggested controller in dealing with the effects of a meal for patients with different weights and avoiding hypoglycemia and hyperglycemia disorders.INDEX TERMS Bergman model, Body-weight effect, Dynamic output controller, Glucose-insulin system, Linear parameter varying (LPV) model.

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“…The fact that T1DM is recognized as an autoimmune disease reduces precise treatment expectations in the short term. For this justification, practical solutions that will facilitate the lives of patients such as artificial pancreas are increasingly being emphasized [8][9][10].…”

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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Control-Oriented Model With Intra-Patient Variations for an Artificial Pancreas

Cited by 18 publications

References 54 publications

PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach

Robust Polytopic-LPV Body-Weight-Dependent Control of Blood Glucose in Type-1 Diabetes

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

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