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Research Papers

Backstepping Sliding Mode Gaussian Insulin Injection Control for Blood Glucose Regulation in Type I Diabetes Patient

[+] Author and Article Information
Akshaya Kumar Patra

Department of Electrical and
Electronics Engineering,
ITER,
Siksha ‘O’Anusandhan University,
Bhubaneswar 751030, Odisha, India
e-mail: hiakp@yahoo.com

Pravat Kumar Rout

Department of Electrical and Electronics
Engineering,
ITER,
Siksha ‘O’Anusandhan University,
Bhubaneswar 751030, Odisha, India
e-mail: pkrout_india@yahoo.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received February 20, 2017; final manuscript received February 19, 2018; published online April 9, 2018. Assoc. Editor: Sergey Nersesov.

J. Dyn. Sys., Meas., Control 140(9), 091006 (Apr 09, 2018) (15 pages) Paper No: DS-17-1111; doi: 10.1115/1.4039483 History: Received February 20, 2017; Revised February 19, 2018

New efforts have been made to build up prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type I diabetes mellitus (TIDM) patients with the development of clinically accurate continuous glucose monitors, automated micro-insulin dispenser (MID), and control algorithms. There is an urgency to develop new control algorithm to determine the desired dose of insulin for maintaining normal BG levels. As a solution to the above issue, a novel backstepping sliding mode Gaussian controller (BSMGC) is proposed whose gains vary dynamically with respect to the error signal. A feedback control law is formulated by a hybrid approach based on BSMGC. A ninth-order linearized state-space model of a nonlinear TIDM patient with the MID is formulated for the design of the BSMGC. This controller is evaluated, and the results are compared with other recently published control techniques. The output responses clearly reveal the better performance of the proposed method to control the BG level within the range of normoglycaemia in terms of accuracy, robustness and handling uncertainties.

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References

Chee, F. , and Fernando, T. , 2003, “ Closed-Loop Glucose Control in Critically Ill Patients Using Continuous Glucose Monitoring System (CGMS) in Real Time,” IEEE Trans. Inf. Technol. Biomed., 7(1), pp. 43–53. [CrossRef] [PubMed]
Chee, F. , Fernando, T. L. , Savkin, A. V. , and Van Heeden, V. , 2003, “ Expert PID Control System for Blood Glucose Control in Critically Ill Patients,” IEEE Trans. Inf. Technol. Biomed., 7(4), pp. 419–425. [CrossRef] [PubMed]
Sutradhar, A. , Chaudhuri, A. S. , Bera, S. C. , and Sadhu, S. , 2002, “ Analysis and Design of an Optimal PID Controller for Insulin Dispenser System,” J. Inst. Eng. (India): Electr. Eng. Div., 82, pp. 304–313.
Ibbini, M. , 2006, “ The PI-Fuzzy Logic Controller for the Regulation of Blood Glucose Level in Diabetic Patients,” J. Med. Eng. Technol., 30(2), pp. 83–92. [CrossRef] [PubMed]
Singh, M. , and Gupta, J. R. P. , 2007, “ A New Algorithm-Based Type-2 Fuzzy Controller for Diabetic Patient,” Int. J. Biomed. Eng. Technol., 1(1), pp. 18–40. [CrossRef]
Gallardo, H. , and Ana, G. , 2013, “ High-Order Sliding-Mode Control for Blood Glucose: Practical Relative Degree Approach,” Control Eng. Pract., 21(5), pp. 747–758. [CrossRef]
Rmileh, A. , and Gabin, W. , 2012, “ Wiener Sliding-Mode Control for Artificial Pancreas: A New Nonlinear Approach to Glucose Regulation,” Comput. Methods Programs Biomed., 107(2), pp. 327–340. [CrossRef] [PubMed]
Kaveh, P. , and Yuri, B. , 2008, “ Blood Glucose Regulation Using Higher-Order Sliding Mode Controller,” Int. J. Robust Nonlinear Control, 18(4–5), pp. 557–569. [CrossRef]
Sutrdhar, A. , and Chaudhuri, S. , 2004, “ Adaptive LQG/LTR Controller for Implantable Insulin Delivery System in Type-1 Diabetes Patient,” Third International Conferences on Systems Identification and Control Problems (SICPRO 04), Moscow, Russia, Jan. 28–30, pp. 1313–1328.
Patra, A. K. , and Rout, P. K. , 2015, “ An Automatic Insulin Infusion System Based on LQG Control Technique,” Int. J. Biomed. Eng. Technol., 17(3), pp. 252–275. [CrossRef]
Chee, F. , and Andrey, V. , 2005, “ Optimal H∞ Insulin Injection Control for Blood Glucose Regulation in Diabetic Patients,” IEEE Trans. Biomed. Eng., 52(10), pp. 1625–1631. [CrossRef] [PubMed]
Patra, A. K. , and Rout, P. K. , 2014, “ Optimal H-Infinity Insulin Injection Control for Blood Glucose Regulation in IDDM Patient Using Physiological Model,” Int. J. Autom. Control, 8(4), pp. 309–322. [CrossRef]
Gampetelli, G. , and Lombarte, M. , 2013, “ Extended Adaptive Predictive Controller With Robust Filter to Enhance Blood Glucose Regulation in Type I Diabetic Subjects,” Comput. Chem. Eng., 59, pp. 243–251. [CrossRef]
Patra, A. K. , and Rout, P. K. , 2017, “ Adaptive Continuous-Time Model Predictive Controller for Implantable Insulin Delivery System in Type I Diabetic Patient,” Optim. Control Appl. Methods, 38(2), pp. 184–204. [CrossRef]
Khalil, H. K. , 2002, Nonlinear Systems, 3rd ed., Prentice Hall, Upper Saddle River, NJ.
Adhikary, N. , and Mahanta, C. , 2013, “ Integral Backstepping Sliding Mode Control for Underactuated Systems: Swing-Up and Stabilization of the Cart-Pendulum System,” ISA Trans., 52(6), pp. 870–880. [CrossRef] [PubMed]
Guyton, J. R. , and Foster, R. O. , 1978, “ A Model of Glucose-Insulin Homeostasis in Man That Incorporates the Heterogenous Fast Pool Theory of Pancreatic Insulin Release,” Diabetes Care, 27(10), pp. 1027–1042. [CrossRef]
Barger, M. , and Rodbard, D. , 1989, “ Computer Simulation of Plasma Insulin and Glucose Dynamics After Subcutaneous Insulin Injection,” Diabetes Care, 12(10), pp. 725–736. [CrossRef] [PubMed]
Parker, R. S. , and Doyle , F. J., III , 1999, “ A Model-Based Algorithm for BG Control in Type 1 Diabetic Patients,” IEEE Trans., Biomed. Eng., 46(2), pp. 148–157. [CrossRef]
Doyle, F. J. , and Peppas, N. A. , 1997, “ Variable-Rate Implantable Insulin Infusion Pumps—Closed Loop Maintenance of Normoglycaemia Under Patient Variability for Type 1 Diabetes,” Artif. Organs, 21(6), p. 495.
Lehmann, E. D. , and Deutsch, T. , 1992, “ Physiological Model of Glucose–Insulin Interaction in Type-1 Diabetes Mellitus,” J. Biomed. Eng., 14(3), pp. 235–242. [CrossRef] [PubMed]
Lehmann, E. D. , and Deutsch, T. , 1998, “ Compartmental Models for Glycaemic Prediction and Decision Support in Clinical Diabetes Care: Promise and Reality,” Comput. Methods Programs Biomed., 56(2), pp. 193–204. [CrossRef] [PubMed]
Spera, G. , 1997, “ Biosensor Research Targets Medical Diagnostics,” Medical Device and Diagnostic Industry Magazine.
Cochin, L. , and Cadwallender, W. , 1997, Analysis and Design of Dynamic Systems, 3rd ed., Addison-Wesley, New York.
Sutradhar, A. , and Chaudhuri, S. , 2005, “ Linear State-Space Model of Physiological Process in a Type-1 Diabetic Patient With Closed Loop Glucose Regulation,” J. AMSE France Adv. Model. Anal. Ser. C, 66(3), pp. 1–18.
Sinha, A. , 2007, Linear Systems: Optimal and Robust Control, CRC Press, Boca Raton, FL.

Figures

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Fig. 1

Closed-loop control of TIDM patient with artificial pancreas

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Fig. 2

Compartmental model of glucose metabolism process with closed-loop control

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Fig. 3

Simplified patient model of GI interaction

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Fig. 4

simulink model for BG regulation in TIDM patient model

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Fig. 5

Glucose production rate of the digestive system as a function of time: (a) gastric emptying rate for 10 g carbohydrate intake (Ch<Chcrit), (b) gastric emptying rate for 60 g carbohydrate intake (Ch>Chcrit), and (c) glucose absorption rate by the gut for 10 g and 60 g carbohydrate intake

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Fig. 6

simulink diagram of different subsystems of TIDM patient model: (a) the gastric emptying subsystem, (b) the CNS subsystem, and (c) the kidney subsystem

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Fig. 7

simulink diagram of MID

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Fig. 8

Block diagram of the patient model

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Fig. 9

Time domain response of the patient model with meal disturbance at 600 min and exercise at 1300 min: (a) GI profile, (b) other related profiles, and (c) Gren rate as a function of BG level

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Fig. 10

State-space model of BSMGC for the BG regulation in TIDM patient

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Fig. 11

Boundary layer around a sliding hyperplane

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Fig. 12

simulink diagram of TIDM patient model with BSMGC

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Fig. 13

Response of the closed-loop patient model: (a) GI profile, (b) other related profiles, and (c) Gren rate as a function of BG level

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Fig. 14

Blood glucose levels with different sliding parametersγ

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Fig. 15

Blood glucose regulation with variation of model parameters and meal disturbance: (a) BG profiles with variations of sh, (b) BG profiles with variations of sp, (c) BG profiles with variations of Q1, and (d) BG profiles with variations of w1(t)

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Fig. 16

Frequency response analysis of the patient model with and without controller: (a) frequency response of patient model (without control) and (b) frequency response of patient model (with BSMGC)

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Fig. 17

Comparative graphical demonstration of the proposed control strategy with respect to other existing optimal control techniques like PID [3], LQG [10], H∞[12], and MPC [14]: (a) variations of BG level in TIDM patient with BSMG control (proposed), PID control [3], LQG control [10], H∞ control [12], and MPC [14] and (b) variations of insulin dose in TIDM patient with BSMG control (proposed), PID control [3], LQG control [10], H∞ control [12], and MPC [14]

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