Self-Triggered Control in Artificial Pancreas

Abstract

The management of type 1 diabetes has been revolutionized by the artificial pancreas system (APS), which automates insulin delivery based on continuous glucose monitor (CGM). While conventional closed-loop systems rely on CGM data, which leads to higher energy consumption at the sensors and increased data redundancy in the underlying communication network. In contrast, this paper proposes a self-triggered control mechanism that can potentially achieve lower latency and energy efficiency. The model for the APS consists of a state and input-constrained dynamical system affected by exogenous meal disturbances. Our self-triggered mechanism relies on restricting the state evolution within the robust control invariant of such a system at all times. To that end, using tools from reachability, we associate a safe time interval with such invariant sets, which denotes the maximum time for which the invariant set remains invariant, even without transmission of CGM data at all times.