An LMI-based Robust Fuzzy Controller for Blood Glucose Regulation in Type 1 Diabetes

TL;DR

This paper develops a robust fuzzy control algorithm based on LMI for artificial pancreas systems in type 1 diabetes, improving glucose regulation by handling input saturation and disturbances.

Contribution

It introduces an LMI-based robust fuzzy controller design using Takagi-Sugeno models for blood glucose regulation in type 1 diabetes, incorporating input saturation considerations.

Findings

Controller effectively maintains glucose levels within desired range.

Simulation results confirm robustness against disturbances.

Optimal gain selection minimizes insulin disturbance effects.

Abstract

This paper presents a control algorithm for creating an artificial pancreas for type 1 diabetes, factoring in input saturation for a practical application. By utilizing the parallel distributed compensation and Takagi-Sugeno Fuzzy model, we design an optimal robust fuzzy controller. Stability conditions derived from the Lyapunov method are expressed as linear matrix inequalities, allowing for optimal controller gain selection that minimizes disturbance effects. We employ the minimal Bergman and Tolic models to represent type 1 diabetes glucose-insulin dynamics, converting them into corresponding Takagi-Sugeno fuzzy models using the sector nonlinearity approach. Simulation results demonstrate the proposed controller's effectiveness.