Robust H∞ control of T–S fuzzy blood glucose regulation system via adaptive event-triggered scheme. (May 2023)

• Record Type: Journal Article
• Authors: Yan, Shen; Chu, Li-li; Cai, Yue
• Is Part Of: Biomedical signal processing and control. Volume 83(2023)
• Publication Date: 2023-05
• Subjects: Blood glucose control -- Event-triggered scheme -- T–S fuzzy system -- Artificial pancreas -- H∞ control; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104643
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: This article investigates the blood glucose regulation problem of type 1 diabetes patients embedded with an artificial pancreas based on an adaptive event-triggered mechanism. The blood glucose regulation system with parameter uncertainties is modeled by an uncertain Takagi–Sugeno (T–S) fuzzy system and a robust H ∞ controller is designed to drive the artificial pancreas to regulate the insulin infusion. The releasing of feedback control signals is determined via an adaptive event-triggered mechanism. In this mechanism, a dynamic triggering threshold can be regulated adaptively according to the system dynamics. Particularly, when the blood glucose of patients is increased by eating some meals, the triggering threshold will be reduced to transmit more signals to maintain the glucose in a safe level. Without requiring all Lyapunov matrices to be positive, new less conservative conditions for ensuring the H ∞ stability of closed-loop fuzzy systems are obtained by linear matrix inequalities (LMIs). Then, a co-designed strategy is presented to derive the controller gains and the triggering matrix simultaneously. The effectiveness of the developed approach is shown by some simulation results. Highlights: A T–S fuzzy modeling method is applied to describe the nonlinear BGRS with parameter uncertainties. An adaptive ETS (AETS) with a dynamic triggering threshold is presented to regulate the data releasing adaptively. New less conservative conditions for ensuring the H ∞ stability of closed-loop fuzzy systems are obtained by linear matrix inequalities (LMIs).