An ensemble nonlinear model predictive control algorithm in an artificial pancreas for people with type 1 diabetes

Boiroux, Dimitri; Hagdrup, Morten; Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik; Jørgensen, John Bagterp

doi:10.1109/ecc.2016.7810604

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…Further assorted applications include estimation in systems biology [124,122], biomechanics [18], optimal control of bodily processes [24,57], signal processing [117], and machine learning [113].…”

Section: Optimization and Simulation In Science And Engineeringmentioning

confidence: 99%

CasADi: a software framework for nonlinear optimization and optimal control

Andersson

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

2,616

976

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We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last seven years.

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Section: Optimization and Simulation In Science And Engineeringmentioning

confidence: 99%

CasADi: a software framework for nonlinear optimization and optimal control

Andersson

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

2,616

976

View full text Add to dashboard Cite

show abstract

“…The applied model consists of different submodels: core model to describe the glucose-insulin dynamics [46]; carbohydrate (CHO) absorption submodel [47]; and insulin absorption submodel [47]. The application of mixed models are frequent in the scientific literature [48]- [50]. The CHO and insulin sub-models have been proposed by Hovorka et al originally in [47], [51].…”

Section: Model Description and Problem Declaration A Model Descriptionmentioning

confidence: 99%

Control of Diabetes Mellitus by Advanced Robust Control Solution

et al. 2019

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The current article investigates the possibilities of designing a robust control solution for a control problem related to type 1 diabetes mellitus. The proposed control methodology exploits linear parameter varying, linear matrix inequality, tensor product model transformation and extended Kalman filtering, four advanced control methods in order to guarantee hard safety control constraints for diabetic patients. In this research we have applied an extension of the minimal model to simulate the glucose-insulin dynamics and the glucose and insulin absorption of a diabetic patient. We have validated our results on numerical simulations by using realistic patient data. During the evaluation we have used randomized glucose intakes both in time and amount (as ''unfavorable'' disturbance signals). Furthermore, we did a long-term (30 days) assessment to confirm the stability of the proposed framework. The results have shown that the developed controller effectively intervenes into the process and provides appropriate control action by avoiding hypoglycemia thereby satisfying the predefined quantity and quality requirements.INDEX TERMS Extended Kalman filter, linear matrix inequality, linear parameter varying-based control, tensor product model transformation, type 1 diabetes mellitus.

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“…The core is the minimal model describing the glucose-insulin dynamics [10] ((7e)-(7g)), while the CHO and insulin absorption submodels are coming from [11] ((7a)-(7d)). The application of mixed models are frequent in the scientific literature [12]- [14]. The CHO and insulin sub-models have been proposed by Hovorka et al originally in [11], [15].…”

Section: A the Extended Minimal Modelmentioning

confidence: 99%

Robust H_∞ controller design for T1DM based on relaxed LMI conditions

Eigner

Bojthe

Mészáros

et al. 2019

2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES)

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The aim of this research is to introduce an advanced controller design method which utilizes the Linear Parameter Variable (LPV) and Linear Matrix Inequality (LMI) theorems in order to control a given physiological model related to Diabetes Mellitus (DM). The developed approach is applied on a modified version of the so-called Minimal Model describes Type 1 DM condition. Due to the applied LPV-LMI conditions the resulting controller uses state feedback kind control rule. Further, robust and optimal control requirements have been included during the declaration of LMI rules allowing the formalization of complex requirements. The resulting control structure is robust from the considered disturbances points of view. During the validation we have found that the controller was able to handle highly unfavorable loads beside satisfying the predefined requirements.

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An ensemble nonlinear model predictive control algorithm in an artificial pancreas for people with type 1 diabetes

Cited by 15 publications

References 33 publications

CasADi: a software framework for nonlinear optimization and optimal control

CasADi: a software framework for nonlinear optimization and optimal control

Control of Diabetes Mellitus by Advanced Robust Control Solution

Robust H_∞ controller design for T1DM based on relaxed LMI conditions

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An ensemble nonlinear model predictive control algorithm in an artificial pancreas for people with type 1 diabetes

Cited by 15 publications

References 33 publications

CasADi: a software framework for nonlinear optimization and optimal control

CasADi: a software framework for nonlinear optimization and optimal control

Control of Diabetes Mellitus by Advanced Robust Control Solution

Robust H∞ controller design for T1DM based on relaxed LMI conditions

Contact Info

Product

Resources

About

Robust H_∞ controller design for T1DM based on relaxed LMI conditions