Induced L2-norm minimization of glucose–insulin system for Type I diabetic patients

Kovács, Levente; Benyó, Balázs; Bokor, József; Benyó, Zoltán

doi:10.1016/j.cmpb.2010.06.019

Cited by 65 publications

(48 citation statements)

References 53 publications

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“…However, the nonlinearity of the system is difficult to handle for a linear controller alone. Linear Parameter Varying (LPV) controllers could capture the dynamics of the glucose-insulin interaction well Kovács et al (2011) Pena et al (2011, making direct nonlinear control possible. The combination of the two approaches have high potential for glucose control Kovács et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Linear Matrix Inequality-based Robust Controller design for Type-1 Diabetes Model

Szalay

Eigner

Kovács

2014

IFAC Proceedings Volumes

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This paper investigates the capabilities of a sophisticated robust nonlinear controller designed directly for a widely known and used high-order nonlinear type 1 diabetes (T1DM) model to lessen the dependency from patient compliance and to answer practical requirements such as avoiding hypoglycaemia. The resulting controller can perform adequately in nominal conditions, but expected to keep this performance even in extreme situations, e.g. high carbohydrate intake, rejecting hypoglycaemic episodes.

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Section: Introductionmentioning

confidence: 99%

Linear Matrix Inequality-based Robust Controller design for Type-1 Diabetes Model

Szalay

Eigner

Kovács

2014

IFAC Proceedings Volumes

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“…This would associate a confidence interval to any prediction, which is of high value. These results could be applied in the context of robust control such as proposed by, e.g., Kovács et al, Chee et al, or Palumbo et al [37][38][39].…”

Section: Resultsmentioning

confidence: 99%

A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes

Bock

François

Gillet

2015

Computer Methods and Programs in Biomedicine

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c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 8 ( 2 0 1 5 ) [107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123] j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b However, it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements. The main contribution of this work is to propose a simple and identifiable linear dynamical model, which is based on the static prediction model of standard therapy. It is shown that the model parameters are intrinsically correlated with physician-set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities compared to state-of-the-art models: a sign of an appropriate model structure and superior reliability.The validation of the proposed dynamic model is performed using data from the UVa simulator and real clinical data, and potential uses of the proposed model for state estimation and BG control are discussed.

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“…As a result, it is enough to design specified number of controllers to the determining point of the p-space. Thus, if the convexity properties are fulfilled, the resulting controller (as convex combination of the designed controllers) can handle each occurring LTI system inside the polytope [10,11]. The benefits of these methods are the drawbacks at the same time: the necessary deep mathematical knowledge and understanding, high computational capacity.…”

Section: Affine Lpv Configurationmentioning

confidence: 99%

“…The applied reference was persistent in time, with values of r = [10,15,14] T and the initial state values were x 0 = [20, 20, 20] T . In order to avoid singularity during calculation of K, we applied ε = 1e − 5 limit (−1e − 5 < e(t) < 1e − 5 → K = K re f , otherwise K is calculated as in (16).…”

Section: Control Of a Simple Nonlinear Systemmentioning

confidence: 99%

“…The drawback is the high computational capacity, complex switching schedule (as p(t) varies over time) and the necessary advanced methods providing global stability. Instead of this natural, however sometimes unmanageable configuration, the polytopic model configuration and polytopic controller design spread out in control engineering [3,[9][10][11].…”

Section: Affine Lpv Configurationmentioning

confidence: 99%

See 1 more Smart Citation

Novel LPV-based Control Approach for Nonlinear Physiological Systems

2017

APH

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The current paper introduces a novel controller design approach dealing with the control of affine Linear Parameter Varying (LPV) systems using the abstract mathematical properties of the LPV parameter space and classical state-feedback design. By the designed controller structure the parameter dependent LPV system mimics a given selected reference LTI system reaching given performance specifications originally prescribed for the reference LTI system. Further, the actual feedback gains are calculated by comparison to the reference control gains -thus, realizing a "relative control". The method is demonstrated on given nonlinear biomedical problems with simulation results under MATLAB.

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Induced L2-norm minimization of glucose–insulin system for Type I diabetic patients

Cited by 65 publications

References 53 publications

Linear Matrix Inequality-based Robust Controller design for Type-1 Diabetes Model

Linear Matrix Inequality-based Robust Controller design for Type-1 Diabetes Model

A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes

Novel LPV-based Control Approach for Nonlinear Physiological Systems

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