A simple two-compartment model for analysis of feedback control of glucose regulation

Martinez-Martinez, F.; Vernon‐Carter, E.J.; Álvarez‐Ramírez, José

doi:10.1016/j.bspc.2021.103363

Cited by 1 publication

(1 citation statement)

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“…The proposed application caters the effects produced during the process of meal intakes and exercise periods on the blood–glucose concentration [ 10 ]. A two-compartmental model is designed using a minimal model for plasma–glucose concentrations for healthy and T1D patients [ 11 ]. In a study, authors presented an alternative approach for subcutaneous insulin infusion.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Model-Based Effective Approach for Glycemic Control in Type-1 Diabetes

Khaqan

Nauman

Shuja

et al. 2022

Sensors

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Type-1 diabetes mellitus (T1DM) is a challenging disorder which essentially involves regulation of the glucose levels to avoid hyperglycemia as well as hypoglycemia. For this purpose, this research paper proposes and develops control algorithms using an intelligent predictive control model, which is based on a UVA/Padova metabolic simulator. The primary objective of the designed control laws is to provide an automatic blood glucose control in insulin-dependent patients so as to improve their life quality and to reduce the need of an extremely demanding self-management plan. Various linear and nonlinear control algorithms have been explored and implemented on the estimated model. Linear techniques include the Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR), and nonlinear control strategy includes the Sliding Mode Control (SMC), which are implemented in this research work for continuous monitoring of glucose levels. Performance comparison based on simulation results demonstrated that SMC proved to be most efficient in terms of regulating glucose profile to a reference level of 70 mg/dL compared to the classical linear techniques. A brief comparison is presented between the linear techniques (PID and LQR), and nonlinear technique (SMC) for analysis purposes proving the efficacy of the design.

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Section: Introductionmentioning

confidence: 99%