Self-Triggered Control in Artificial Pancreas

TL;DR

This paper introduces a self-triggered control approach for artificial pancreas systems that aims to reduce energy consumption and latency by minimizing unnecessary data transmissions while maintaining glucose regulation.

Contribution

It proposes a novel self-triggered control mechanism based on reachability and invariant sets, enhancing energy efficiency in artificial pancreas systems.

Findings

Reduces sensor energy consumption

Maintains glucose regulation with less frequent data updates

Provides a robust control framework for APS

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.