Semiglobal Sampled-Data Dynamic Output Feedback Controller for the Glucose–Insulin System

Ferdinando, Mario Di; Pepe, Pierdomenico; Palumbo, Pasquale; Panunzi, Simona; Gaetano, Andrea De

doi:10.1109/tcst.2018.2881662

Cited by 21 publications

(5 citation statements)

References 62 publications

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“…It is important to note that the architecture of Figure 1 is also used to validate different mathematical models of glucose-insulin regulators that include a meal simulation model specifically designed for these systems [31,32]. Moreover, this architecture has been demonstrated capable to describe the medical needs of T2DM patients with a very high accuracy and constitutes the bases of the UVA/Padova Type 1 Diabetes Simulator [33], recognized by the U.S. FDA as a valid alternative to animal tests and extensively used in the AP literature [6,8,[34][35][36].…”

Section: Virtual Clinical Environmentmentioning

confidence: 99%

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FPGA-Based Implementation of a Digital Insulin-Glucose Regulator for Type 2 Diabetic Patients

Di Patrizio Stanchieri,

De Marcellis,

Faccio

et al. 2024

Electronics

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This paper reports on the hardware implementation of a digital insulin-glucose regulator for type 2 diabetic patients by using a Field Programmable Gate Array board. For a real time-control of the patient insulin concentration, the insulin-regulator needs to measure only his blood glucose concentration. With respect to other reported solutions using general-purpose programmable hardware’s, the proposed insulin-glucose regulator allows to design a software-free, fully-hardware architecture of the system here described in detail. A prototype has been developed so to validate its functionality in the following two operating modes: (i) in the open loop condition for which only the insulin-glucose regulator is operating; (ii) in the closed loop condition for which the insulin-glucose regulator acting as an artificial pancreas is connected to a population of one hundred virtual patients individuated by employing a comprehensive theoretical model recognized by the U.S. Food and Drug Administration for the pre-clinical validation of glucose control strategies. These virtual patients present the same trend of the variation of the glucose concentration achieving different maximum and minimum values of glucose concentrations when eating a meal. The paper presents and discusses the experimental results by comparing them with those ones obtained by implementing the theoretical model through numerical simulations performed in SIMULINK. Relative errors lower than ±1% have been achieved by performing this comparison so demonstrating a very high accuracy of the proposed insulin-glucose regulator digital system. The implemented hardware solution of the digital controller can process the input data related to the glucose concentration of each virtual patient in about 1.1 μs with an estimated power consumption of about 36 mW. These achievements open the way for further investigations on digital architectures for glucose regulators to be integrated in VLSI as System-on-Chips and/or Lab-on-Chips for portable, wearable, and implantable solutions in real biomedical applications.

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Section: Virtual Clinical Environmentmentioning

confidence: 99%

“…demonstrated capable to describe the medical needs of T2DM patients with a very high accuracy and constitutes the bases of the UVA/Padova Type 1 Diabetes Simulator [33], recognized by the U.S. FDA as a valid alternative to animal tests and extensively used in the AP literature [6,8,[34][35][36].…”

Section: Virtual Clinical Environmentmentioning

confidence: 99%

FPGA-Based Implementation of a Digital Insulin-Glucose Regulator for Type 2 Diabetic Patients

Di Patrizio Stanchieri,

De Marcellis,

Faccio

et al. 2024

Electronics

Self Cite

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“…It should be stressed that the use of compact models for controller design purposes is well celebrated in the field [35], [36]. More specifically, gain-personalized models have been previously used in the literature as a useful tool to account for the variability of total daily insulin requirement among patients [37], [14], [38].…”

Section: A Control-oriented Patient Modelmentioning

confidence: 99%

Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation

Sanz

Garcia

Díez

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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This paper is focused on closed-loop control of postprandial glucose levels of patients with type 1 diabetes mellitus after unannounced meals, still a major challenge towards a fully autonomous artificial pancreas. The main limitations are the delays introduced by the subcutaneous insulin pharmacokinetics and the glucose sensor, which typically lead to insulin over-delivery. Current solutions reported in the literature typically resort to meal announcement, which requires the patient intervention. In this paper, a disturbance observer is used to estimate the effect of unannounced meals and the insulin pharmacokinetics is taken into account by means of a feedforward compensator. The proposed strategy is validated in silico with the UVa/Padova metabolic simulator. It is demonstrated how the disturbance observer successfully estimates and counteracts not only the effect of meals but also sudden drops in the glucose levels that may lead to hypoglycemia. For unannounced meals, results show a median time-in-range of 80% in a 30-day scenario with high carbohydrate content and large intra-subject variability. Optionally, users may decide to announce meals. In this case, considering severe bolus mismatch due to carbohydrate counting errors, the median time-in-range is increased up to 88%. In every case, hypoglycemia is avoided.

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“…For real-time control of the BGC in the AP system, insulin measurement is not possible. To overcome the drawback as mentioned earlier by exploiting only glucose measurements, the sampled-data dynamic output-feedback controller for T2DM patients proposed in Di Ferdinando et al (2018) was used, where the control law requires both the current glucose measurement and a past value of the glucose concentration. For T1DM, in Nath et al (2019), a nonlinear observer is designed for estimating the states, and then the control law is derived using feedback linearization and regional pole placement technique.…”

Section: Introductionmentioning

confidence: 99%

“…Estimating the states of BMM without linearization and observer-based controllers was proposed using an unscented Kalman filter in Eberle and Ament (2011) and an extended Kalman filter in Eberle and Ament (2012). Using the sampled-data control theory in Di Ferdinando et al (2020), Pepe et al (2017), andDi et al (2017), sampled-data controllers for T2DM patients are presented, in which measurements of glucose and insulin concentrations are utilized. For real-time control of the BGC in the AP system, insulin measurement is not possible.…”

Section: Introductionmentioning

confidence: 99%

Sample-data output-feedback parameter variable control of glucose for type 1 diabetes mellitus patients

Hooshmandi

Bayat

2022

Transactions of the Institute of Measurement and Control

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This paper presents a new approach for nonlinear sampled-data output-feedback control of blood glucose concentration (BGC) in patients with type 1 diabetes mellitus (T1DM). The Bergman minimal model (BMM) describing the glucose-insulin regulatory system is framed as a linear parameter-varying (LPV) model that the sampled glucose measurement with the quantized insulin infusion is considered as input delay. The proposed method utilizes the input-delay approach based on a parameter-dependent Lyapunov–Krasovskii functional (LKF) and provides the parameterized matrix inequality conditions for the design of the controller. Using the slack variables, the design conditions are converted to a new form that can achieve feasible solutions by solving a limited number of bilinear matrix inequality. The proposed architecture guarantees stability in a compact set of states for all the trajectories with the parametric uncertainty to address the issue of inter-patient variability. Simulation results confirm that the proposed sampled-data static output-feedback LPV controller with only glucose measurement regulates the BGC despite the parametric uncertainty and meal disturbances in T1DM patients.

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Semiglobal Sampled-Data Dynamic Output Feedback Controller for the Glucose–Insulin System

Cited by 21 publications

References 62 publications

FPGA-Based Implementation of a Digital Insulin-Glucose Regulator for Type 2 Diabetic Patients

FPGA-Based Implementation of a Digital Insulin-Glucose Regulator for Type 2 Diabetic Patients

Artificial Pancreas System With Unannounced Meals Based on a Disturbance Observer and Feedforward Compensation

Sample-data output-feedback parameter variable control of glucose for type 1 diabetes mellitus patients

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