Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas

TL;DR

This paper introduces a switching nonlinear model predictive control algorithm combined with maximum likelihood estimation for a dual-hormone artificial pancreas, demonstrating effective glucose regulation in simulated type 1 diabetes patients.

Contribution

It presents a novel control algorithm and model identification method tailored for dual-hormone artificial pancreases, improving glucose regulation accuracy.

Findings

Achieved 89.3% time in range in simulations

No hypoglycemic events occurred in tests

Validated control and identification methods on 50 virtual patients

Abstract

In this work, we present a switching nonlinear model predictive control (NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use maximum likelihood estimation (MLE) to identify model parameters. A dual-hormone AP consists of a continuous glucose monitor (CGM), a control algorithm, an insulin pump, and a glucagon pump. The AP is designed with a heuristic to switch between insulin and glucagon as well as state-dependent constraints. We extend an existing glucoregulatory model with glucagon and exercise for simulation, and we use a simpler model for control. We test the AP (NMPC and MLE) using in silico numerical simulations on 50 virtual people with type 1 diabetes. The system is identified for each virtual person based on data generated with the simulation model. The simulations show a mean of 89.3% time in range (3.9-10 mmol/L) and no hypoglycemic events.