Proof of Concept Control of a T1DM Model Using Robust Fixed-Point Transformations via Sliding Mode Differentiators

被引:0
作者
Czako, Bence [1 ]
Drexler, Daniel Andras [1 ]
Kovacs, Levente [1 ]
机构
[1] Obuda Univ, Physiol Controls Res Ctr, H-1034 Budapest, Hungary
关键词
type 1 diabetes mellitus; artificial pancreas; automated insulin delivery; robust fixed-point transformations; sliding mode differentiators; ARTIFICIAL PANCREAS; PEDIATRIC-PATIENTS; ADAPTIVE-CONTROL; TYPE-1;
D O I
10.3390/math11051210
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Type 1 Diabetes Mellitus (T1DM) is a disease where insulin production is obstructed in the pancreas, and exogenous administration of the hormone must be utilized. Automatic control of the administration can be achieved using the Artificial Pancreas (AP) concept, whose performance is heavily reliant on the underlying control algorithm. A Robust Fixed-Point Transformations (RFPT)-based control strategy was designed to automate the insulin delivery process, which incorporates a Sliding Mode Differentiator (SMD) to provide higher order derivatives of the blood glucose level. Inter-patient variability, carbohydrate disturbances, and real-life sampling were included in the validation of the method. Results showed that the algorithm could regulate the blood glucose level, with a significant overshoot at the beginning of the control action due to the adaptive nature of the controller. Results indicate that the design requires additional modifications to be feasible in practice, including an extended validation with more virtual patients and realistic simulation settings in the future. Nevertheless, the current control algorithm has several attractive features, which are discussed with respect to PID and Model Predictive Control (MPC).
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页数:21
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共 32 条
  • [1] Positioning time in range in diabetes management
    Advani, Andrew
    [J]. DIABETOLOGIA, 2020, 63 (02) : 242 - 252
  • [2] [Anonymous], 2020, CONSUMER GUIDE INSUL
  • [3] Discrete differentiators based on sliding modes
    Barbot, Jean-Pierre
    Levant, Arie
    Livne, Miki
    Lunz, Davin
    [J]. AUTOMATICA, 2020, 112
  • [4] Barbot JP, 2016, IEEE INT WORK VAR, P166, DOI 10.1109/VSS.2016.7506910
  • [5] Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis
    Bekiari, Eleni
    Kitsios, Konstantinos
    Thabit, Hood
    Tauschmann, Martin
    Athanasiadou, Eleni
    Karagiannis, Thomas
    Haidich, Anna-Bettina
    Hovorka, Roman
    Tsapas, Apostolos
    [J]. BMJ-BRITISH MEDICAL JOURNAL, 2018, 361
  • [6] Safety and Feasibility of the OmniPod Hybrid Closed-Loop System in Adult, Adolescent, and Pediatric Patients with Type 1 Diabetes Using a Personalized Model Predictive Control Algorithm
    Buckingham, Bruce A.
    Forlenza, Gregory P.
    Pinsker, Jordan E.
    Christiansen, Mark P.
    Wadwa, R. Paul
    Schneider, Jennifer
    Peyser, Thomas A.
    Dassau, Eyal
    Lee, Joon Bok
    O'Connor, Jason
    Layne, Jennifer E.
    Ly, Trang T.
    [J]. DIABETES TECHNOLOGY & THERAPEUTICS, 2018, 20 (04) : 257 - 262
  • [7] Chakrabarty A, 2020, IEEE T CONTR SYST T, V28, P2600, DOI [10.1109/TCST.2019.2939122, 10.1109/tcst.2019.2939122]
  • [8] Control of a T1DM Model Using Robust Fixed-Point Transformations Based Control With Disturbance Rejection
    Czako, Bence
    Drexler, Daniel Andras
    Kovacs, Levente
    [J]. PROCEEDINGS OF 2022 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR 2022), 2022, : 343 - 348
  • [9] Discrete time derivation of the Robust Fixed-Point Transformation method
    Czako, Bence Geza
    Drexler, Daniel Andras
    Kovacs, Levente
    [J]. IFAC PAPERSONLINE, 2022, 55 (01): : 535 - 540
  • [10] Continuous time Robust Fixed Point Transformations based control
    Czako, Bence Geza
    Drexler, Daniel Andras
    Kovacs, Levente
    [J]. 2019 IEEE AFRICON, 2019,