Direct continuous time system identification of MISO transfer function models applied to type 1 diabetes

Kirchsteiger, Harald; Pölzer, Stephan; Johansson, Rolf; Renard, Éric; Re, Luigi del

doi:10.1109/cdc.2011.6161344

Cited by 23 publications

(12 citation statements)

References 17 publications

(23 reference statements)

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“…An adequate mathematical model of human metabolism -like the ones presented in Magni et al (2009) and Wilinska et al (2010) -is usually rather complicated, where verification and parameter identification alone are challenging tasks Kirchsteiger et al (2011), while model-based control algorithms must handle nonlinearity and inaccuracy alike. The commercially available continuous glucose measurement (CGM) sensor has significant measurement noise and five minutes sampling time Battelino and Bolinder (2008).…”

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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“…7 shows results for three MPC setups: no meal disturbance lookahead for MPC 1 and MPC 3 while MPC 2 has this functionality. Furthermore, MPC 3 delivers only control signals quantized in 0.5 unit steps by rounding the result of the optimization (10).…”

Section: Resultsmentioning

confidence: 99%

“…For the MPC, we employ an ARX type of model. The MBBC model is a control oriented model which was first introduced in [10] and further refined in [11]. The derivation of the models for the MPC design and the model for the MBBC is described in section II.…”

Section: Introductionmentioning

confidence: 99%

A model based bolus calculator for blood glucose control in type 1 diabetes

Kirchsteiger

2014

2014 American Control Conference

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We propose a model based bolus calculator which, based on an identified model, information on the carbohydrate content of the meals and an initial single glucose measurement, computes the optimal insulin bolus dose in order to minimize the blood glucose deviations. We use a simulation model for identification of models for control and closed-loop simulations. The performance of the proposed bolus calculator is compared with a model predictive control approach which uses continuous glucose information and insulin injection. Both methods achieve comparable performance for some patients while the insulin amount, number of injections and computational complexity is significantly reduced for the bolus calculator.

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“…To date several types of glucose metabolism models have been proposed for the purpose of simulation and in-silico trial, glucose changes prediction and hypo-, hyperglycaemia early detection (see, e.g., Cobelli et al, 2009 for a comprehensive review). Most of these efforts were first-principles-based descriptions of diabetes physiology (Bergman, Phillips, & Cobelli, 1981;Dalla Man, Rizza, & Cobelli, 2007;Wilinska et al, 2005) and only to a lesser extent mathematical modelling by means of system identification (Cescon, Ståhl, Landin-Olsson, & Johansson, 2009;Cescon, Stemmann, & Johansson, 2012;Finan, Jorgensen, Poulsen, & Madsen, 2010;Kirchsteiger, Castillo Estrada, Pölzer, del Re, & Renard, 2011;Kirchsteiger, Pölzer, Johansson, Renard, & del Re, 2011;van Heudsen, Dassau, Zisser, Seborg, & Doyle, 2012). However, while many of these models exhibit good predictive performances, less attention has been dedicated to the fundamental aspects of estimating correct signs and time constants of the identified models' impulse responses as pointed out in Ståhl (2012).…”

Section: Motivationmentioning

confidence: 99%

Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics

Cescon

Johansson

Renard

et al. 2014

International Journal of Control

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One of the main limiting factors in improving glucose control for type 1 diabetes mellitus (T1DM) subjects is the lack of a precise description of meal and insulin intake effects on blood glucose. Knowing the magnitude and duration of such effects would be useful not only for patients and physicians, but also for the development of a controller targeting glycaemia regulation. Therefore, in this paper we focus on estimating low-complexity yet physiologically sound and individualised multi-input single-output (MISO) models of the glucose metabolism in T1DM able to reflect the basic dynamical features of the glucose-insulin metabolic system in response to a meal intake or an insulin injection. The models are continuous-time second-order transfer functions relating the amount of carbohydrate of a meal and the insulin units of the accordingly administered dose (inputs) to plasma glucose evolution (output) and consist of few parameters clinically relevant to be estimated. The estimation strategy is continuous-time data-driven system identification and exploits a database in which meals and insulin boluses are separated in time, allowing the unique identification of the model parameters. © 2014 © 2014 Taylor & Francis

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Direct continuous time system identification of MISO transfer function models applied to type 1 diabetes

Cited by 23 publications

References 17 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 model based bolus calculator for blood glucose control in type 1 diabetes

Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics

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