2014 American Control Conference 2014
DOI: 10.1109/acc.2014.6858980
|View full text |Cite
|
Sign up to set email alerts
|

A model based bolus calculator for blood glucose control in type 1 diabetes

Abstract: We propose a model based bolus calculator which, based on an identified model, information on the carbohydrate content of the meals and an initial single glucose measurement, computes the optimal insulin bolus dose in order to minimize the blood glucose deviations. We use a simulation model for identification of models for control and closed-loop simulations. The performance of the proposed bolus calculator is compared with a model predictive control approach which uses continuous glucose information and insul… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
3
2
1

Relationship

1
5

Authors

Journals

citations
Cited by 8 publications
(3 citation statements)
references
References 16 publications
0
3
0
Order By: Relevance
“…The advantage of using model structure (2) is that the parameters of the model have a clear physiological meaning (see also [15]). The constant K 1 describes the effect of 1 gram of carbohydrates on the BG, whereas K 2 predicts the effect of 1 IU of bolus insulin (both for t → ∞).…”
Section: Model Structurementioning
confidence: 99%
“…The advantage of using model structure (2) is that the parameters of the model have a clear physiological meaning (see also [15]). The constant K 1 describes the effect of 1 gram of carbohydrates on the BG, whereas K 2 predicts the effect of 1 IU of bolus insulin (both for t → ∞).…”
Section: Model Structurementioning
confidence: 99%
“…A traditional model predictive control assuming the ARX model with disturbance input, manipulated variable constraints, and state estimation using the conventional Kalman filter was reported in [20]. Another typical application of the unconstrained model predictive control for the state-space model obtained by transforming the nonparametric impulse-response model along with the state estimation based on the steady-state Kalman filter was presented in [21].…”
Section: State Of the Artmentioning
confidence: 99%
“…The model can be individualized with few FIT-related parameters. Following this idea, [9], [56]- [58] introduced a second-order with integrator transfer function to describe subjects under MDI. Even though initially the authors considered two different poles for each insulin and CHO subsystem, they observed that the BG behavior is similar when considering equal poles in each subsystem, so a reduced model is finally obtained with four parameters to be identified.…”
Section: Control-oriented Models: a Reviewmentioning
confidence: 99%