Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance

• Published in: Automatica (Oxford) 2018-05, Vol.91, p.105-117
• Main Authors: Gondhalekar, Ravi, Dassau, Eyal, Doyle, Francis J.
• Format: Article
• Language: English
• Subjects: Artificial pancreas; Model predictive control; Nonlinear optimization; Safety-critical control; State-dependent costs
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2018.01.025

A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues. Velocity-weighting MPC reduces the occurrence of controller-induced hypoglycemia. Velocity-penalty MPC yields more effective hyperglycemia correction. Benefits of the proposed MPC law over the MPC strategy deployed in the authors’ previous clinical trial campaign are demonstrated via a comprehensive in-silico analysis. The proposed MPC law was deployed in four distinct US Food & Drug Administration approved clinical trial campaigns, the most extensive of which involved 29 subjects each spending three months in closed-loop. The paper includes implementation details, an explanation of the state-dependent cost functions required for velocity-weighting and penalties, a discussion of the resulting nonlinear optimization problem, a description of the four clinical trial campaigns, and control-related trial highlights.