Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A model-based algorithm for blood glucose control in type I diabetic patients

R S Parker1, F J Doyle, N A Peppas

  • 1Department of Chemical Engineering, University of Delaware, Newark 19716, USA.

IEEE Transactions on Bio-Medical Engineering
|February 5, 1999
PubMed
Summary

Model-based predictive control algorithms can maintain normal blood glucose levels in Type I diabetes patients. Advanced controllers using state estimation significantly improve glucose control accuracy and reduce fluctuations.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Precise control of synthetic hydrogel network structure via linear, independent synthesis-swelling relationships.

Science advances·2021
Same author

Biodegradable cationic nanogels with tunable size, swelling and pK<sub>a</sub> for drug delivery.

International journal of pharmaceutics·2020
Same author

Whole-genome DNA methylation status associated with clinical PTSD measures of OIF/OEF veterans.

Translational psychiatry·2017
Same author

An inulin and doxorubicin conjugate for improving cancer therapy.

Journal of drug delivery science and technology·2014
Same author

Improvements of growth, appetite, and physical activity in helminth-infected schoolboys 6 months after single dose of albendazole.

Asia Pacific journal of clinical nutrition·2014
Same author

Note: Parameter-independent bounding of the stochastic Michaelis-Menten steady-state intrinsic noise variance.

The Journal of chemical physics·2013

Area of Science:

  • Biomedical Engineering
  • Control Systems Engineering
  • Endocrinology

Background:

  • Maintaining normoglycemia is critical for Type I diabetes management.
  • Insulin infusion pumps require sophisticated control algorithms to prevent hyperglycemia and hypoglycemia.
  • Existing control strategies face challenges with patient variability and external disturbances.

Purpose of the Study:

  • To develop and evaluate a model-based predictive control (MPC) algorithm for closed-loop blood glucose regulation in Type I diabetes.
  • To enhance controller performance through state estimation and advanced internal models.
  • To assess the algorithm's efficacy in disturbance rejection and setpoint tracking.

Main Methods:

  • Construction of a 19th-order nonlinear pharmacokinetic-pharmacodynamic (PK/PD) model of the diabetic patient.

Related Experiment Videos

  • Linear system identification using Laguerre basis projection for noisy patient data.
  • Development of a linear MPC controller based on the identified model.
  • Design of a second, advanced MPC controller incorporating state estimation and a Kalman filter.
  • Evaluation of controller performance during simulated oral glucose tolerance tests and under varying noise conditions.

    • Main Results:

    • The advanced MPC controller with state estimation maintained glucose levels within 15 mg/dl of the setpoint despite measurement noise.
    • Under noise-free conditions, the MPC with state estimation demonstrated significant improvements over a literature-based internal model controller, reducing undershoot by 49.4% and settling time by 45.7%.
    • The controller effectively rejected unmeasured disturbances, such as those from an oral glucose tolerance test.

    Conclusions:

    • Model-based predictive control algorithms show significant potential for improving automated blood glucose management in insulin infusion pumps.
    • State estimation and advanced internal models enhance the robustness and accuracy of glucose control.
    • This approach offers a promising avenue for developing more effective artificial pancreas systems.