Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control

Bhattacharjee, Arpita; Easwaran, Arvind; Leow, Melvin Khee–Shing; Cho, Nam‐Joon

doi:10.1016/j.bspc.2017.12.002

Cited by 13 publications

(21 citation statements)

References 33 publications

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“…In future, the performance of the multi-objective controller with proper exercise and activity model may be studied on in silico patients. Table 6 Meal time and sizes as given in [23] 24 h 3:00 am 3 two 75 gm at 7:00 am and 1:00 pm. One 50 gm at 8:00 pm.…”

Section: Resultsmentioning

confidence: 99%

“…• Thus the proposed controller gives better performance with lesser hyperglycaemic and hypoglycaemic events and with a lesser amount of insulin infusion without using SM and IFL. Table 5 Comparison of controller responses with meal protocols and controller strategies [19] Controller performance Protocol #1 Protocol #2 As reported in [19] postprandial (PP) As reported in [19] overall (O) Proposed controller As reported in [19] postprandial (PP) As reported in [19] • Case 2: The performance of the proposed controller is also compared with the performance of fully-automated offline-and online tuned IMC and semi-automated online-tuned IMC [23]. The patient was subjected to the same meal disturbances as in scenario 1 [23] and is given in Table 6.…”

Section: Performance Comparison With Other Controllers From Earlier Rmentioning

confidence: 99%

“…Many researchers have developed a different mathematical model for designing the controller for controlling the BG in closed-loop fashion both through the IV and SC routes [2][3][4][5]. Researchers also worked on various closed-loop control algorithms for BG regulation [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] both through IV and SC routes. Parker et al [6] designed an automatic H-infinity (H ∞ ) controller for controlling the BG level through the intravenous route considering Sorensen physiological model [3] and showed that controller tracks BG level very well.…”

Section: Introductionmentioning

confidence: 99%

“…Here patients experience hypoglycaemia 0% of total simulation time. o The performance of the proposed multi‐objective controller is compared with the performance of the H ∞ controller with safety mechanism (SM) and insulin feedback loop (IFL), tested on in silico adults in the T1DM simulator as reported in [19]. The proposed controller gives better performance with lesser hyperglycaemic and hypoglycaemic events and with a lesser amount of insulin infusion without using SM and IFL. o The performance of the designed controller is also compared with fully‐automated online‐tuned controller based on internal model control (IMC) strategy [23]. The designed multi‐objective controller keeps the BG level of in silico patient more time within the clinically safe target zone (70–180 mg/dl) than this fully‐automated online‐tuned controller.…”

Section: Introductionmentioning

confidence: 99%

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Robust multi‐objective blood glucose control in Type‐1 diabetic patient

Mandal

Sutradhar

2019

IET Systems Biology

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In this study, an automatic robust multi-objective controller has been proposed for blood glucose (BG) regulation in Type-1 Diabetic Mellitus (T1DM) patient through subcutaneous route. The main objective of this work is to control the BG level in T1DM patient in the presence of unannounced meal disturbances and other external noises with a minimum amount of insulin infusion rate. The multi-objective output-feedback controller with H ∞ , H 2 and pole-placement constraints has been designed using linear matrix inequality technique. The designed controller for subcutaneous insulin delivery was tested on in silico adult and adolescent subjects of UVa/Padova T1DM metabolic simulator. The experimental results show that the closed-loop system tracks the reference BG level very well and does not show any hypoglycaemia effect even during the long gap of a meal at night both for in silico adults and adolescent. In the presence of 50 gm meal disturbance, average adult experience normoglycaemia 92% of the total simulation time and hypoglycaemia 0% of total simulation time. The robustness of the controller has been tested in the presence of irregular meals and insulin pump noise and error. The controller yielded robust performance with a lesser amount of insulin infusion rate than the other designed controllers reported earlier.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Comparison With Other Controllers From Earlier Rmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust multi‐objective blood glucose control in Type‐1 diabetic patient

Mandal

Sutradhar

2019

IET Systems Biology

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“…Large variants of control techniques exist for the control of blood glucose of T1DM patients for different patient models. Some of the relevant literature review is presented here, proportional integral derivative, 13 model predictive control, 27,28 fuzzy logic control, 29 adaptive control, 9,[30][31][32] and so on. These control techniques have been successfully and extensively implemented for the considered problem without addressing intrapatient variability issue explicitly in the controller design.…”

Section: Introductionmentioning

confidence: 99%

Robust guaranteed‐cost output feedback control of blood glucose in type 1 diabetes patient with intrapatient variability

Nath

Dey

2020

Optim Control Appl Methods

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Summary Dealing with the interpatient and the intrapatient variability in type 1 diabetes mellitus (T1DM) patient are the prominent control challenges in the development of the artificial pancreas system (APS). An APS consider a nonlinear patient model that has many state variables, but the reality is that out of the many states only glucose concentration is measurable till date without any complication and undue high cost. In the present work, the philosophy is to evolve a control technique that can address the above‐cited challenges or issues. In the sequel, a robust observer is first designed based on the attractive ellipsoid method (AEM) for norm‐bounded uncertainty structure. The information of the estimated states is then used to design a guaranteed‐cost robust output feedback control. The design methodology approximates the nonlinearity present in the model as quasi‐one‐sided Lipschitz thus including more information of nonlinearity in the design as well as the control problem is recast into a linear matrix inequality (LMI) framework. Numerical simulations of 200 virtual T1DM patients are carried out which reveal that there are no cases of hypoglycemia and postprandial hyperglycemia in the presence of bounded parametric variability and high uncertainty in exogenous meal disturbance. The proposed method is simple, efficient and optimal closed‐loop solution for blood glucose regulation in T1DM patients in a robust nonlinear framework.

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Observer‐based controller design for blood glucose regulation for type 1 diabetic patients with disturbance estimation: A backstepping approach

Homayounzade

2024

Asian Journal of Control

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In this paper, an observer‐based nonlinear controller is proposed to regulate blood glucose concentration (BGC) in type 1 diabetes mellitus (T1DM). The considered virtual patient model is the extended Bergmann minimal model, which is augmented by a meal disturbance and adapted to represent the insulin–glucose homeostasis of T1DM. The backstepping (BS) technique is used to design a closed‐loop feedback controller. The proposed controller does not need to measure insulin, plasma concentrations, and external disturbances while improving control performance and robustness against uncertainty. Insulin concentrations and plasma levels are estimated using state observers and disturbance using a disturbance estimator. These estimations are used as feedback to the controller. The asymptotic stability of the observer‐based controller is proved using the Lyapunov theorem. Furthermore, it is proven that the system is bounded input–bounded output (BIBO) stable in the presence of uncertainties caused by uncertain parameters and external disturbances. For real situations, we consider only the BGC available for measurement, and in addition, inter‐ and intra‐patient variability of system parameters is considered. The results confirm that the proposed controller can asymptotically regulate BGC through appropriate injection of insulin under meal disturbance and of variations in system parameters.

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Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control

Cited by 13 publications

References 33 publications

Robust multi‐objective blood glucose control in Type‐1 diabetic patient

Robust multi‐objective blood glucose control in Type‐1 diabetic patient

Robust guaranteed‐cost output feedback control of blood glucose in type 1 diabetes patient with intrapatient variability

Observer‐based controller design for blood glucose regulation for type 1 diabetic patients with disturbance estimation: A backstepping approach

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