Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems.

ABSTRACT: OBJECTIVE:The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus. In this paper, we aim to lower the energy consumption of the AP by reducing controller updates, that is, the number of times the decision-making algorithm is invoked to compute an appropriate insulin dose. METHODS:Physiological insights into glucose management are leveraged to design an event-triggered model predictive controller (MPC) that operates efficiently, without compromising patient safety. The proposed event-triggered MPC is deployed on a wearable platform. Its robustness to latent hypoglycemia, model mismatch, and meal misinformation is tested, wit

SUBMITTER: Chakrabarty A 

PROVIDER: S-EPMC5839516 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature