Comparison of model Predictive control (MPC) algorithms to optimise blood glucose in fully closed loop (FCL) systems.

Abstract

Model Predictive Control (MPC) is emerging within fully closed loop (FCL) systems to offer a promising advancement, by automating glucose regulation for people with Type 1 Diabetes. This article assesses the clinical effectiveness of FCL systems and explores future optimisations by comparison of recent developed systems. Evidence suggests that MPC-based FCL systems outperform hybrid closed-loop (HCL) models using Proportional-Integral-Derivative (PID) control, achieving higher time-in-range (TIR, 74.4% vs. 63.7%, P = 0.020) and better postprandial glucose regulation. However, no system has consistently surpassed the clinical TIR target (>70%), with postprandial hyperglycaemia and insulin absorption delays remaining key challenges. Three recent emerging FCL advancements include nonlinear MPC (NMPC) for dual-hormone systems, integrating glucagon to reduce hypoglycaemia, λ-Policy Iteration (λ-PI), an adaptive reinforcement learning model, and pulse-modulated artificial pancreas (PMCL) systems, which mimic natural insulin secretion. We compare features of these three emerging solutions and propose a novel hybrid model which combines benefits from these algorithms, to improve accuracy. While these innovations show promise in in-silico models, clinical validation is lacking. Key barriers include glucagon instability, CGM inaccuracies, cost, and patient adherence. Future research must prioritise long-term trials incorporating real-world factors such as exercise and dietary variability. By integrating predictive control, adaptive learning, and dual-hormone regulation, FCL systems could transform diabetes management, bridging the gap between technology and full automation.