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UID:430@cran.univ-lorraine.fr
DTSTART;TZID=Europe/Paris:20240625T140000
DTEND;TZID=Europe/Paris:20240625T150000
DTSTAMP:20240621T083806Z
URL:https://www.cran.univ-lorraine.fr/events/seminaire-antonio-ferramosa/
SUMMARY:Séminaire Antonio Ferramosa
DESCRIPTION:- Titre : Artificial Pancreas under stable MPC designs: towards
  a fully-automated blood-glucose regulation.\n\n- Résumé : Type 1 Diabet
 es Mellitus (T1DM) is a chronic disease\, affecting approximately 42 milli
 on people around the world (10%–15% of all diabetes cases). It is charac
 terized by a destruction of pancreatic β cells due to an auto-immune resp
 onse leading to a complete deficiency of endogenous insulin production\, t
 hus resulting in higher blood glucose (BG) levels (fasting BG: &gt\; 126 m
 g/dL\, postprandial BG (2 hs): ≥ 200 mg/dL). In order to restore euglyce
 mia levels (fasting BG: 80–130 mg/dL\, peak postprandial BG (2 hs): &lt\
 ; 180 mg/dL) the functional insulin treatment (FIT) was proposed as therap
 eutic way to administer exogenous insulin injections\, imitating healthy s
 ecretion patterns. This way\, a basal insulin is delivered to keep glycemi
 a flat during fasting periods\, while a bolus insulin is delivered to coun
 teract postprandial hyperglycemia or bring high glucose levels back to tar
 get. The automatic insulin delivery based on glycemia measurement (artific
 ial 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 monito
 ring (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.\nThis talk presents 
 a pulsatile Zone Model Predictive Control (pZMPC) for the control of blood
  glucose concentration (BGC) in patients with Type 1 Diabetes Mellitus (T1
 DM). The main novelties of the algorithm – in contrast to other existing
  strategies – are: (i) it controls the patient glycemia by injecting sho
 rt duration insulin boluses for both\, the basal and bolus infusions\, in 
 an unified manner\,(ii) it performs the predictions and estimations (criti
 cal 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 pati
 ents from the FDA-accepted UVA/Padova simulation platform.\n\n- Biographie
  : Antonio Ferramosca was born in Maglie (LE)\, Italy\, in 1982. He is an 
 Associate Professor at the Department of Management\, Information and Prod
 uction Engineering of the University of Bergamo (Italy). He received his m
 aster’s degrees in computer science engineering from the University of P
 avia (Italy) in 2006\, and the Ph.D. degree in Control Engineering from th
 e University of Seville (Spain) in 2011. He was a Postdoctoral Fellow and 
 then Research Associate at the Argentinean National Scientific and Technic
 al Research Council (CONICET)\, (from 2012 to 2020). He serves as Associat
 e Editor for the Optimal Control Applications and Methods journal. He is t
 he author and co-author of more than 100 publications\, including journal 
 papers\, book chapters\, and conference proceedings. His research interest
 s 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.\n\n&nb
 sp\;\n\nTEAMS link: https://teams.microsoft.com/l/meetup-join/19%3ameeting
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CATEGORIES:Département CID,Séminaire projet MODELE
LOCATION:CRAN - ENSEM\, 2\, Avenue de la Foret de Haye\, Voandoeuvre-les-Na
 ncy\, 54516\, France
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