Comparison of Prediction Models for a Dual-Hormone Artificial Pancreas∗∗Funded by the Danish Diabetes Academy supported by the Novo Nordisk Foundation. Contact information: John Bagterp J_rgensen(jbjo@dtu.dk).
Open accessAbstract
In this paper we compare the performance of five different continuous time transfer function models used in closed-loop model predictive control (MPC). These models describe the glucose-insulin and glucose-glucagon dynamics. They are discretized into a state-space description and used as prediction models in the MPC algorithm. We simulate a scenario including meals and daily variations in the model parameters. The numerical results do not show significant changes in the glucose traces for any of the models, excepted for the first order model. From the present study, we can conclude that the second order model without delay should provide the best trade-off between sensitivity to uncertainties and practical usability for in vivo clinical studies.
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Bergman, R. N., Phillips, L. S., & Cobelli, C. (1981) · Journal of Clinical Investigation · doi:10.1172/jci110398 · Cited by 1,640 · see citing works
Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes
Hovorka, R., Canonico, V., Chassin, L. J., Haueter, U., Massi-Benedetti, M., Federici, M. O., Pieber, T. R., Schaller, H. C., Schaupp, L., Vering, T., et al. (2004) · Physiological Measurement · doi:10.1088/0967-3334/25/4/010 · Cited by 1,274 · see citing works
Model predictive control of glucose concentration in type I diabetic patients: An in silico trial
Magni, L., Raimondo, D., Man, C., Nicolao, G. D., Kovatchev, B., & Cobelli, C. (2009) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2009.04.003 · Cited by 190 · see citing works
Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors
Breton, M. D., & Kovatchev, B. P. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200517 · Cited by 173 · see citing works
Multivariable Adaptive Identification and Control for Artificial Pancreas Systems
Turksoy, K., Quinn, L., Littlejohn, E., & Çınar, A. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2291777 · Cited by 162 · see citing works
Control-Relevant Models for Glucose Control Using A Priori Patient Characteristics
van Heusden, K., Dassau, E., Zisser, H., Seborg, D. E., & Doyle, F. J. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2176939 · Cited by 160 · see citing works
Pillonetto, G., Sparacino, G., & Cobelli, C. (2003) · Mathematical Biosciences · doi:10.1016/s0025-5564(03)00044-0 · Cited by 94 · see citing works
Percival, M. W., Bevier, W., Wang, Y.-Q., Dassau, E., Zisser, H., Jovanovic, L., & Doyle, F. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400522 · Cited by 61 · see citing works
Herrero, P., Georgiou, P., Oliver, N., Reddy, M., Johnston, D., & Toumazou, C. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700416 · Cited by 55 · see citing works
Estimating Interval Process Models for Type 1 Diabetes for Robust Control Design
Kirchsteiger, H., Estrada, G. C., Pölzer, S., Renard, É., & del Re, L. (2011) · IFAC Proceedings Volumes · doi:10.3182/20110828-6-it-1002.03770 · Cited by 44 · see citing works
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Glucose Sensing for Diabetes Monitoring: Recent Developments
Bruen, D., Delaney, C., Florea, L., & Diamond, D. (2017) · Sensors · doi:10.3390/s17081866 · Cited by 778 · see citing works
Overnight glucose control in people with type 1 diabetes
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Zhang, Y., Sun, J., Liu, L., & Qiao, H. (2021) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2021.107929 · Cited by 49 · see citing works
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Rebro, M., Tárník, M., Miklovičová, E., & Murgaš, J. (2018) · ELEKTRO · doi:10.1109/elektro.2018.8398363 · Cited by 1 · see citing works
Self-citationRodríguez-Herrero, A., E, C., García-Sáez, G., ELENA, M., & Orozco-López, O. (2017) · International Conference on Advances in Mechanical and Robotics Engineering (AMRE) · doi:10.15224/978-1-63248-123-8-19
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- Authors
- Boiroux, D., Bátora, V., Hagdrup, M., Tárnik, M., Murgaš, J., Schmidt, S., Nørgaard, K., Poulsen, N. K., Madsen, H., & Jørgensen, J. B.
- Affiliations
- Danish Diabetes Academy (DK)
- Slovak University of Technology in Bratislava (SK)
- Technical University of Denmark (DK)
- Hvidovre Hospital (DK)
- ORCID
- Venue
- IFAC-PapersOnLine
- Publisher
- Elsevier BV
- Published
- 2015
- Volume
- 48
- Issue
- 20
- Pages
- 7-12
- ISSN
- 2405-8963 (ISSN-L)
- OpenAlex
- W2475121651
- Cited by
- 16
- References
- 14
- Citations by year
- 2026: 1 · 2022: 1 · 2021: 1 · 2020: 1 · 2018: 4 · 2017: 5 · 2016: 3
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- IFAC-PapersOnLine (publishedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
- Keywords
- artificial pancreas, transfer function model, glucose-insulin dynamics, state space discretization, meal disturbance, parameter variation, model uncertainty, first-order model, second-order model
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