Adaptive model predictive control for a dual-hormone artificial pancreas

Boiroux, Dimitri; Batora, Vladimir; Hagdrup, Morten; Wendt, Sabrina Lyngbye; Poulsen, Niels Kjølstad; Madsen, Henrik; Jørgensen, John Bagterp

doi:10.1016/j.jprocont.2018.05.003

Cited by 38 publications

(23 citation statements)

References 61 publications

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“…Many version of MPCs have been implemented in AP systems. They can be classified as [13]: unconstrained MPC [14], multiple-model adaptive MPC [15], zone MPC [16], set-point-based enhanced MPC (eMPC) [17], adaptive MPC [18,19], bi-hormonal MPC [20] and policy-based stochastic MPC [21,22]. These strategies were tested under in-silico simulators (UVA/Padova [23], Cambridge [24], Medtronic Virtual Patient [25]) and also, under in-vivo clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Abuin

Rivadeneira

Ferramosca

et al. 2020

Journal of Process Control

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Section: Introductionmentioning

confidence: 99%

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Abuin

Rivadeneira

Ferramosca

et al. 2020

Journal of Process Control

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“…Remark 2 While bi‐hormonal (insulin and glucagon) artificial pancreas systems are also considered in some studies (see e.g. [33]), due to medical efficacy and technological feasibility, single hormone artificial pancreas systems are usually used where their actuation is only the insulin infusion. In this paper, a single artificial pancreas system is designed and therefore, no insulin is injected by the pump to the patient if the computed control action is negative.…”

Section: Resultsmentioning

confidence: 99%

Blood glucose concentration control for type 1 diabetic patients: a multiple‐model strategy

Batmani

Khodakaramzadeh

2020

IET Systems Biology

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In this study, a multiple-model strategy is evaluated as an alternative closed-loop method for subcutaneous insulin delivery in type 1 diabetes. Non-linearities of the glucose-insulin regulatory system are considered by modelling the system around five different operating points. After conducting some identification experiments in the UVA/Padova metabolic simulator (accepted simulator by the US Food and Drug Administration (FDA)), five transfer functions are obtained for these operating points. Paying attention to some physiological facts, the control objectives such as the required settling time and permissible bounds of overshoots and undershoots are determined for any transfer functions. Then, five PID controllers are tuned to achieve these objectives and a bank of controllers is constructed. To cope with difficulties of the presence of delays in subcutaneous blood glucose (BG) measuring and in administration of insulin, a glucose-dependent setpoint is considered as the desired trajectory for the BG concentration. The performance of the obtained closed-loop glucose-insulin regulatory system is investigated on the in silico adult cohort of the UVA/Padova metabolic simulator. The obtained results show that the proposed multiple-model strategy leads to a closed-loop mechanism with limited hyperglycemia and no severe hypoglycemia.

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“…Selecting a noise model involves a trade-off between conflicting requirements namely those of low variance set-point tracking, disturbance rejection and fast response to unmeasured disturbances. Boiroux et al (2015) provided a comparative study of the effects of choosing different deterministic model parts in MPC-based Artificial Pancreas technology keeping the stochastic part fixed. The goal of the present paper is to study the role played by the stochastic part of the model.…”

Section: Introductionmentioning

confidence: 99%

On the significance of the noise model for the performance of a linear MPC in closed-loop operation

et al. 2016

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This paper discusses the significance of the noise model for the performance of a Model Predictive Controller when operating in closed-loop. The process model is parametrized as a continuous-time (CT) model and the relevant sampled-data filtering and control algorithms are developed. Using CT models typically means less parameters to identify. Systematic tuning of such controllers is discussed. Simulation studies are conducted for linear time-invariant systems showing that choosing a noise model of low order is beneficial for closed-loop performance.

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Adaptive model predictive control for a dual-hormone artificial pancreas

Cited by 38 publications

References 61 publications

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Blood glucose concentration control for type 1 diabetic patients: a multiple‐model strategy

On the significance of the noise model for the performance of a linear MPC in closed-loop operation

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