Robust Model-Based Control of Blood Glucose Concentration in Type 1 Diabetic Patients
Schriftenreihe Advances in Mechatronics, Band 22
1st Edition
Published on 12. December 2013
Book
181 pages
978-3-99033-258-0 (ISBN)
Description
This book addresses the problem of data-based modeling and model-based control design for blood glucose (BG) concentration for type 1 diabetes.
Since the 1970s, there has been a considerable effort in the development of an artificial pancreas system which autonomously delivers the correct insulin profile in order to normalize BG. However, up to date no such systems are available. Three major challenges in its development are addressed in this book: the reliability of continuous glucose sensor data, the identification of patient specific models for control design, and the availability of models for closed-loop simulations under realistic conditions. Furthermore, the development of control algorithms is given consideration.
A novel method for sensor recalibration based on LMIs is presented, which improves the accuracy significantly. A model identification technique specifically accounting for the variability was developed and applied retrospectively to clinical data. Several control strategies are proposed, not only focusing on the setup with continuous measurements and control action, but also on the case of single insulin injections.
Since the 1970s, there has been a considerable effort in the development of an artificial pancreas system which autonomously delivers the correct insulin profile in order to normalize BG. However, up to date no such systems are available. Three major challenges in its development are addressed in this book: the reliability of continuous glucose sensor data, the identification of patient specific models for control design, and the availability of models for closed-loop simulations under realistic conditions. Furthermore, the development of control algorithms is given consideration.
A novel method for sensor recalibration based on LMIs is presented, which improves the accuracy significantly. A model identification technique specifically accounting for the variability was developed and applied retrospectively to clinical data. Several control strategies are proposed, not only focusing on the setup with continuous measurements and control action, but also on the case of single insulin injections.