An opportunity of using Robust Fixed Point Transformation-based controller design in case of Type 1 Diabetes Mellitus

Kovács, Levente; Eigner, György; Czakó, Bence; Siket, Máté; Tar, József K.

doi:10.1109/sa47457.2019.8938069

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Furthermore, in [37], a simple approach was suggested to simulate lower order control strategies for higher order systems to evade non-monotonic fluctuation of the error terms; a simple proportional error decay rate was simulated for a second order system. A control technology-based tackling of treating patients suffering from type 1 diabetes mellitus, an early version of this adaptive control, was investigated via simulations [38].…”

Section: Related Workmentioning

confidence: 99%

Tackling Modeling and Kinematic Inconsistencies by Fixed Point Iteration-Based Adaptive Control

Atinga

Tar

2023

Machines

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The Fixed Point Iteration-based Adaptive Control design methodology is an alternative to the Lyapunov function-based technology. It contains higher-order feedback terms than the standard resolved acceleration rate control. This design approach strictly separates the kinematic and dynamic issues. At first, a purely kinematic prescription is formulated for driving the components of the tracking error to zero. Then an available approximate dynamic model is used to calculate the approximated necessary control forces. Before exerting on the controlled system, these forces are adaptively deformed in order to precisely obtain the prescribed kinematic behavior. The necessary deformation is iteratively found by the use of a contractive map that results in a sequence that converges to the unique fixed point of this map. In the case of underactuated systems, when the relative order of the control task also increases, the highest-order time-derivative depends on the lower-order ones according to the dynamic model of the system. This makes it impossible to realize the arbitrarily constructed kinematic design. In the paper, a resolution to this discrepancy is proposed. The method is demonstrated using two non-linear paradigms, a three-degree-of-freedom robot arm, and a two-degree-of-freedom system, i.e., two coupled non-linear springs. The operation of the method was investigated via simulations made by the use of Julia language and simple sequential programs. It was found that the suggested solution could be considered as a new variant of the fixed point iteration-based model reference adaptive control that is applicable for underactuated systems even if the relative order of the task is increased.

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Section: Related Workmentioning

confidence: 99%

Tackling Modeling and Kinematic Inconsistencies by Fixed Point Iteration-Based Adaptive Control

Atinga

Tar

2023

Machines

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“…Electronics 2024, 13, 1607 2 of 13 strong increase in the need of insulin [3,4]. For T1DM patients various Artificial Pancreas (APs) solutions have been proposed and implemented for in blood glucose regulation to guarantee the best health conditions and, consequently, the day-to-day life quality of diabetic patients [5][6][7][8][9]. Differently, glucose regulator solutions for T2DM are very few in the literature.…”

Section: Introductionmentioning

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

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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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Quantized sampled-data static output feedback control of the glucose–insulin system

Ferdinando

Pepe

Gennaro

et al. 2021

Control Engineering Practice

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An opportunity of using Robust Fixed Point Transformation-based controller design in case of Type 1 Diabetes Mellitus

Cited by 6 publications

References 10 publications

Tackling Modeling and Kinematic Inconsistencies by Fixed Point Iteration-Based Adaptive Control

Tackling Modeling and Kinematic Inconsistencies by Fixed Point Iteration-Based Adaptive Control

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

Quantized sampled-data static output feedback control of the glucose–insulin system

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