Self-Triggered Control in Artificial Pancreas

TL;DR

A safe time interval is associated 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, even without transmission of CGM data at all times.

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.

Figures (2)

• Figure 1: Invariant set for insulin-glucose model
• Figure 2: Solution of the set of initial states $x_0$ obtained from Algorithm 1 for (a) $n=2$ (b) $n=3$ and (c) $n=4$.

Theorems & Definitions (3)

• Definition 1
• Definition 2
• Definition 3