Literature DB >> 33095743

Sliding mode control for a fractional-order non-linear glucose-insulin system.

Muhammad Waleed Khan1, Muhammad Abid2, Abdul Qayyum Khan2, Ghulam Mustafa2, Muzamil Ali3, Asifullah Khan4.   

Abstract

By providing the generalisation of integration and differentiation, and incorporating the memory and hereditary effects, fractional-order modelling has gotten significant attention in the past few years. One of the extensively studied and utilised models to describe the glucose-insulin system of a human body is Bergman's minimal model. This non-linear model comprises of integer-order differential equations. However, comparison with the experimental data shows that the fractional-order version of Bergman's minimal model is a better representative of the glucose-insulin system than its original integer-order model. To design a control law for an artificial pancreas for a diabetic patient using a fractional-order model, different techniques, including feedback linearisation, have been applied in the literature. The authors' previous work shows that the fractional-order version of Bergman's model describes the glucose-insulin system in a better way than the integer-order model. This study applies the sliding mode control technique and then compares the obtained simulation results with the ones obtained using feedback linearisation.

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Year:  2020        PMID: 33095743      PMCID: PMC8687314          DOI: 10.1049/iet-syb.2020.0030

Source DB:  PubMed          Journal:  IET Syst Biol        ISSN: 1751-8849            Impact factor:   1.615


  13 in total

1.  Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes.

Authors:  Scott M Pappada; Brent D Cameron; Paul M Rosman; Raymond E Bourey; Thomas J Papadimos; William Olorunto; Marilyn J Borst
Journal:  Diabetes Technol Ther       Date:  2011-02       Impact factor: 6.118

2.  A model-based algorithm for blood glucose control in type I diabetic patients.

Authors:  R S Parker; F J Doyle; N A Peppas
Journal:  IEEE Trans Biomed Eng       Date:  1999-02       Impact factor: 4.538

3.  A semiclosed-loop algorithm for the control of blood glucose levels in diabetics.

Authors:  M E Fisher
Journal:  IEEE Trans Biomed Eng       Date:  1991-01       Impact factor: 4.538

4.  Estimation of future glucose concentrations with subject-specific recursive linear models.

Authors:  Meriyan Eren-Oruklu; Ali Cinar; Lauretta Quinn; Donald Smith
Journal:  Diabetes Technol Ther       Date:  2009-04       Impact factor: 6.118

5.  MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test.

Authors:  G Pacini; R N Bergman
Journal:  Comput Methods Programs Biomed       Date:  1986-10       Impact factor: 5.428

6.  Parameter estimation of a meal glucose-insulin model for TIDM patients from therapy historical data.

Authors:  Oscar D Sánchez; Eduardo Ruiz-Velázquez; Alma Y Alanís; Griselda Quiroz; Luis Torres-Treviño
Journal:  IET Syst Biol       Date:  2019-02       Impact factor: 1.615

7.  Blood glucose concentration control for type 1 diabetic patients: a multiple-model strategy.

Authors:  Yazdan Batmani; Shadi Khodakaramzadeh
Journal:  IET Syst Biol       Date:  2020-02       Impact factor: 1.615

Review 8.  Artificial pancreas: past, present, future.

Authors:  Claudio Cobelli; Eric Renard; Boris Kovatchev
Journal:  Diabetes       Date:  2011-11       Impact factor: 9.461

9.  Numerical study for multi-strain tuberculosis (TB) model of variable-order fractional derivatives.

Authors:  Nasser H Sweilam; Seham M Al-Mekhlafi
Journal:  J Adv Res       Date:  2015-06-27       Impact factor: 10.479

10.  Blood glucose concentration control for type 1 diabetic patients: a non-linear suboptimal approach.

Authors:  Yazdan Batmani
Journal:  IET Syst Biol       Date:  2017-08       Impact factor: 1.615

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