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

arXiv: https://arxiv.org/abs/2408.10333 · HTML: https://mlsanity.com/paper/2408.10333/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [Neural Networks for on-chip Model Predictive Control: a Method to Build Optimized Training Datasets and its application to Type-1 Diabetes](https://mlsanity.com/paper/2504.11355/) (2025)

## Abstract (arXiv)

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.