Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

• Published in: Journal of process control Vol. 77; pp. 97 - 113
• Main Authors: Hajizadeh, Iman, Rashid, Mudassir, Cinar, Ali
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.05.2019
• Subjects: Adaptive model predictive control; Artificial pancreas system; Dynamic constraint adjustment; Insulin on board; Plasma insulin concentration; Recursive subspace identification; Recursive subspace identification; Insulin on board; Adaptive model predictive control; Artificial pancreas system; Plasma insulin concentration; Dynamic constraint adjustment; recursive subspace identification; plasma insulin concentration; dynamic constraint adjustment; adaptive model predictive control; artificial pancreas system; insulin on board
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2019.03.009

•An adaptive model predictive control (MPC) with dynamic safety constraint.•Recursive subspace-based system identification to identify stable, high-fidelity linear time-varying models from closed-loop data.•Artificial pancreas system with plasma insulin concentration estimation to prevent insulin overdosing.•Simulation case studies to demonstrate the performance of the proposed adaptive MPC algorithm. An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closed-loop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.