Séminaire Antonio Ferramosa


25 juin 2024    
14h00 - 15h00

2, Avenue de la Foret de Haye, Voandoeuvre-les-Nancy, 54516

Type d’évènement

– Titre : Artificial Pancreas under stable MPC designs: towards a fully-automated blood-glucose regulation.

– Résumé : Type 1 Diabetes Mellitus (T1DM) is a chronic disease, affecting approximately 42 million people around the world (10%–15% of all diabetes cases). It is characterized by a destruction of pancreatic β cells due to an auto-immune response leading to a complete deficiency of endogenous insulin production, thus resulting in higher blood glucose (BG) levels (fasting BG: > 126 mg/dL, postprandial BG (2 hs): ≥ 200 mg/dL). In order to restore euglycemia levels (fasting BG: 80–130 mg/dL, peak postprandial BG (2 hs): < 180 mg/dL) the functional insulin treatment (FIT) was proposed as therapeutic way to administer exogenous insulin injections, imitating healthy secretion patterns. This way, a basal insulin is delivered to keep glycemia flat during fasting periods, while a bolus insulin is delivered to counteract postprandial hyperglycemia or bring high glucose levels back to target. The automatic insulin delivery based on glycemia measurement (artificial pancreas, AP) was proposed as a way to implement FIT in a closed-loop manner. It is a medical equipment composed of a continuous glucose monitoring (CGM) sensor, an insulin pump (CSII) and a control algorithm which, based on CGM readings, adjusts the insulin delivery in order to maintain blood glucose concentration in normoglycemia levels.
This talk presents a pulsatile Zone Model Predictive Control (pZMPC) for the control of blood glucose concentration (BGC) in patients with Type 1 Diabetes Mellitus (T1DM). The main novelties of the algorithm – in contrast to other existing strategies – are: (i) it controls the patient glycemia by injecting short duration insulin boluses for both, the basal and bolus infusions, in an unified manner,(ii) it performs the predictions and estimations (critical to anticipate both, hypo and hyperglycemia) based on a physiological individualized long-term model, (iii) it employs disturbance observers to compensate plant-model mismatches, and (iv) it ensures, under standard assumptions, closed-loop stability. This talk will present the design of this control strategy as well as its testing in a cohort of in-silico patients from the FDA-accepted UVA/Padova simulation platform.

– Biographie : Antonio Ferramosca was born in Maglie (LE), Italy, in 1982. He is an Associate Professor at the Department of Management, Information and Production Engineering of the University of Bergamo (Italy). He received his master’s degrees in computer science engineering from the University of Pavia (Italy) in 2006, and the Ph.D. degree in Control Engineering from the University of Seville (Spain) in 2011. He was a Postdoctoral Fellow and then Research Associate at the Argentinean National Scientific and Technical Research Council (CONICET), (from 2012 to 2020). He serves as Associate Editor for the Optimal Control Applications and Methods journal. He is the author and co-author of more than 100 publications, including journal papers, book chapters, and conference proceedings. His research interests are in the field of control theory for constrained linear and nonlinear systems, including Model Predictive Control, and control applications to industrial and biological systems, including Artificial Pancreas.


TEAMS link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_NzYyYjcxYzMtYzBkMi00MDI2LTg4ZjItZTZiZGMyNWYwOTk4%40thread.v2/0?context=%7b%22Tid%22%3a%22158716cf-46b9-48ca-8c49-c7bb67e575f3%22%2c%22Oid%22%3a%22955ae724-3c70-4f81-8a92-327e1dc5af57%22%7d

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