Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: <i>In Silico</i> Trial

“…In the literature, several algorithms have been presented starting from proportional-integral-derivative schemes 1,7 and, more recently, relying on a very promising approach called model predictive control (MPC). 2,[8][9][10][11][12][13][14][15][16][17][18] So far, encouraging pilot results have been reported using proportional-integral-derivative control 1,3 and MPC strategies. 4,6,19 In Breton and coauthors, 20 the MPC algorithm described by Patek and coauthors 18 was in vivo tested.…”

Section: Introductionmentioning

confidence: 99%

Artificial Pancreas: Model Predictive Control Design from Clinical Experience

Toffanin

Messori

Palma

et al. 2013

J Diabetes Sci Technol

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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.

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“…A particular case where this problem can be solved in a general, algorithmic manner is the one of batch processes, where the MPC controller may be asked to track the same reference profile many timesfor example, when maintaining a cooling profile in a crystallizer (Shen et al (1999)). In this paper, we propose to solve this run-to-run (or "batch-to-batch") problem via gradientdescent optimization, noting that a simpler realization of what is essentially the same idea but with a single tuning parameter may be found in the work of Magni et al (2009). Any MPC controller requires a dynamical model of the system, [ŷ k+1 , ...,ŷ k+n ] = f (u k , ..., u k+n−1 ), that is able to predict how the outputs y ∈ R n y ×1 will evolve when driven by the inputs u ∈ R n u ×1 over some discrete prediction interval [k + 1, k + n] 1 .…”

Section: Mpc Formulation and Tuningmentioning

confidence: 99%