Closed-loop control with unannounced exercise for adults with type 1 diabetes using the Ensemble Model Predictive Control

García-Tirado, José; Corbett, John P.; Boiroux, Dimitri; Jørgensen, John Bagterp; Breton, Marc D.

doi:10.1016/j.jprocont.2019.05.017

Cited by 29 publications

(16 citation statements)

References 47 publications

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“…101 All of these inputs may be used to create more sophisticated model-predictive-control algorithms that incorporate pattern recognition and meal detection. 102,103 A second approach would be to innovate on rapid acting insulin delivery with faster PK/PD profiles. 104 In addition to new formations of subcutaneous insulin, alternative delivery pathways (eg, intraperitoneal) may increase the PK/PD for bolus insulin delivery.…”

Section: Correction Bolus Practicesmentioning

confidence: 99%

Diabetes Technology Meeting 2020

Shang

Zhang

Bequette

et al. 2021

J Diabetes Sci Technol

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Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.

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Section: Correction Bolus Practicesmentioning

confidence: 99%

Diabetes Technology Meeting 2020

Shang

Zhang

Bequette

et al. 2021

J Diabetes Sci Technol

View full text Add to dashboard Cite

show abstract

“…Additionally, moderate-intensity exercise is considered from 11 am to 12 am. The effect of this single exercise bout on plasma insulin concentration is characterized as a direct disturbance on the insulin dynamics through a step change with a magnitude of 1.5 mU l −1 min −1 applied to δ I [27].…”

Section: Meal Disturbance and Exercise Simulation Setupmentioning

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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“…Following usual approaches in AP modeling ( 1,17,23 ) both, the insulin and digestion absorption subsystems, are assumed to be represented by linear dynamics, derived from compartmental models. The insulin absorption subsystem consists of two compartments: the subcutaneous and blood compartments.…”

Section: Glucose‐insulin Modelmentioning

confidence: 99%

“…The fact that parameters are physiological may (and in fact does) help in the identification process, given that it is easier to detect mistaken estimations. Although the model considered in this article is essentially different to the others in literature, many other MPC‐based approaches for AP consider this kind of individualization 5,23,25‐27 …”

Section: Glucose‐insulin Modelmentioning

confidence: 99%

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

González

Rivadeneira

Ferramosca

et al. 2020

Optim Control Appl Methods

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Summary In this work, the problem of regulating blood glucose (glycemia) in type I diabetic patients is studied by means of an impulsive zone model predictive control (iZMPC), which bases its predictions on a novel long‐term glucose‐insulin model. Taking advantage of the impulsive version of the model—which features real‐life properties of diabetes patients that some other popular models do not—the given control guarantees the stability under moderate‐to‐severe plant‐model mismatch and disturbances. Long‐term scenarios—including meals and physiological parameter variations—are simulated and the results are satisfactory as every hyperglycemic and hypoglycemic episodes are suitably controlled.

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Closed-loop control with unannounced exercise for adults with type 1 diabetes using the Ensemble Model Predictive Control

Cited by 29 publications

References 47 publications

Diabetes Technology Meeting 2020

Diabetes Technology Meeting 2020

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

Stable impulsive zone model predictive control for type 1 diabetic patients based on a long‐term model

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