Insulin limitation in the Artificial Pancreas by Sliding Mode Reference Conditioning and Insulin Feedback: an in silico comparison

Sala-Mira, I.; Díez, José Luis; Bondia, Jorge

doi:10.1016/j.ifacol.2017.08.1153

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…The identified parameters (see Table 1), are in the range of other results in the literature [17]. Further, the goodness of fit is 84.99% for G(t) and 98.63% for I p (t).…”

Section: Design Modelsupporting

confidence: 71%

“…The Identifiable Virtual Patient (IVP) model [17] is selected to design the main controller and the R A estimator since it is a tradeoff option between Bergman's model [18] and Hovorka's model [19] in terms of accuracy and complexity. Its equations are…”

Section: Design Modelmentioning

confidence: 99%

“…the model gain) is commonly addressed in the literature to personalize control-oriented models, see for example [21]. The identification procedure is based on the two-step approach in [17] to reduce the estimation error in the insulin pharmacokinetic parameters ( 1 , 2 y C I ) for the average patient: first, insulin subsystem parameters were estimated by minimizing the root mean squared error (RMSE) between the estimated plasma insulin and the real value computed by the simulator. The second step minimizes the RMSE between the estimated glucose measurement and the real glucose computed by the simulator to estimate p 2 , GEZI, EGP and S I while insulin pharmacokinetic parameters were kept fixed according to the first step.…”

Section: Design Modelmentioning

confidence: 99%

“…where C H are the ingested carbohydrates (mg), V G is the glucose distribution volume (dL) and m the peak time of glucose appearance (min). To determine those parameters a realistic worst-case scenario is considered: a large meal of C H = 1.2 • 10 5 mg with a fast absorption peak ( m = 47 min, chosen from Table 2 in [17]). V G is selected from the average adult model in the UVa-Padova simulator.…”

Section: Insulin Feedbackmentioning

confidence: 99%

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Sliding-mode disturbance observers for an artificial pancreas without meal announcement

Sala-Mira

Díez

Ricarte

et al. 2019

Journal of Process Control

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Carbohydrate counting is not only a burden for patients with type 1 diabetes, but estimation errors in meal announcement could also degrade the outcomes of the current hybrid closed-loop systems. Therefore, removing meal announcement is desirable. A novel control system is addressed here to face postprandial control without meal announcement. The proposed system grounds on two applications of the sliding mode observers in dealing with disturbances: first, the equivalent output technique is used to reconstruct the meal rate of glucose appearance via a first order sliding mode observer; second, a supertwisting-based residual generator is used to detect the meals. Subsequently, a bolusing algorithm uses the information of the two observers to trigger a series of boluses based on a proportional-derivative-Q1 like strategy. An in silico validation with 30 patients in a 30-day scenario reveals that the meal detector algorithm achieves a low rate of false positives per day (0.1 (0.1), mean (SD)) and a detection time of 28.5(6.2) min. Additionally, the bolusing algorithm fulfills a non-statistically different mean glucose than the hybrid counterpart with bolus misestimation (146.69 (12.20) mg/dL vs. 144.28 (11.01) mg/dL, p>0.05), without increasing hypoglycemia (0.029 (0.077) vs. 0.004 (0.014)%, p > 0,05), although at the expense of a slightly higher time in hyperglycemia (22.51(8.72) % vs. 18.65 (7.89)%, p < 0.05) due to the conservative tuning of the bolusing algorithm for the sake of safety.

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“…The identified parameters (see Table 1), are in the range of other results in the literature [17]. Further, the goodness of fit is 84.99% for G(t) and 98.63% for I p (t).…”

Section: Design Modelsupporting

confidence: 71%

Section: Design Modelmentioning

confidence: 99%

Section: Design Modelmentioning

confidence: 99%

Section: Insulin Feedbackmentioning

confidence: 99%

See 2 more Smart Citations

Sliding-mode disturbance observers for an artificial pancreas without meal announcement

Sala-Mira

Díez

Ricarte

et al. 2019

Journal of Process Control

View full text Add to dashboard Cite

show abstract

“…Most of the hybrid systems constrain the insulin-on-board through gains [33,34,35] or thresholds [36,37,38]; hence the change of the main controller is immediate. In this article, the main controller implements the SAFE-AP controller [25,26,39]. This controller consists of a PID controller with insulin feedback that inhibits the plasma insulin [40] and a safety layer based on a sliding mode reference conditioning that encloses the insulin-on-board below an upper limit [25].…”

Section: Methodsmentioning

confidence: 99%