title: Artificial pancreas: model predictive control design from clinical experience.
abstract: BACKGROUND
  The objective of this research is to develop a new artificial pancreas that takes into account the experience accumulated during more than 5000 h of closed-loop control in several clinical research centers. The main objective is to reduce the mean glucose value without exacerbating hypo phenomena. Controller design and in silico testing were performed on a new virtual population of the University of Virginia/Padova simulator.
  
  
  METHODS
  A new sensor model was developed based on the Comparison of Two Artificial Pancreas Systems for Closed-Loop Blood Glucose Control versus Open-Loop Control in Patients with Type 1 Diabetes trial AP@home data. The Kalman filter incorporated in the controller has been tuned using plasma and pump insulin as well as plasma and continuous glucose monitoring measures collected in clinical research centers. New constraints describing clinical knowledge not incorporated in the simulator but very critical in real patients (e.g., pump shutoff) have been introduced. The proposed model predictive control (MPC) is characterized by a low computational burden and memory requirements, and it is ready for an embedded implementation.
  
  
  RESULTS
  The new MPC was tested with an intensive simulation study on the University of Virginia/Padova simulator equipped with a new virtual population. It was also used in some preliminary outpatient pilot trials. The obtained results are very promising in terms of mean glucose and number of patients in the critical zone of the control variability grid analysis.
  
  
  CONCLUSIONS
  The proposed MPC improves on the performance of a previous controller already tested in several experiments in the AP@home and JDRF projects. This algorithm complemented with a safety supervision module is a significant step toward deploying artificial pancreases into outpatient environments for extended periods of time.
authors.affiliation: Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Toffanin
authors.forename: Mirko
authors.initials: M
authors.lastname: Messori
authors.forename: Federico
authors.initials: F
authors.lastname: Di Palma
authors.forename: Giuseppe
authors.initials: G
authors.lastname: De Nicolao
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
authors.forename: Lalo
authors.initials: L
authors.lastname: Magni
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000628:therapy*; D006801:Humans; D007328:Insulin / Q000008:administration & dosage* / Q000627:therapeutic use*; D007558:Italy; D016014:Linear Models; D008954:Models, Biological; D010045:Outpatients; D019397:Pancreas, Artificial; D013997:Time Factors; D014481:United States
chemical_list: D001786:Blood Glucose; D007328:Insulin