Advances in Artificial Pancreas Systems : Adaptive and Multivariable Predictive Control

Kamuran Turksoy

ISBN 10: 3319722441 / ISBN 13: 9783319722443

Published by Springer International Publishing, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This brief introduces recursive modeling techniques that take account of variations in blood glucose concentration within and between individuals. It describes their use in developing multivariable models in early-warning systems for hypo- and hyperglycemia; these models are more accurate than those solely reliant on glucose and insulin concentrations because they can accommodate other relevant influences like physical activity, stress and sleep. Such factors also contribute to the accuracy of the adaptive control systems present in the artificial pancreas which is the focus of the brief, as their presence is indicated before they have an apparent effect on the glucose concentration and so can be more easily compensated. The adaptive controller is based on generalized predictive control techniques and also includes rules for changing controller parameters or structure based on the values of physiological variables. Simulation studies and clinical studies are reported to illustrate the performance of the techniques presented.

Seller Inventory # 9783319722443

Bibliographic Details

• Title: Advances in Artificial Pancreas Systems : Adaptive and Multivariable Predictive Control
• Publisher: Springer International Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 3319722441
• ISBN 13: 9783319722443
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 8D millimeters 235 millimeters width by 155 millimeters height by 8 millimeters depth
• Weight: 241 grams

About this title

Synopsis

This brief introduces recursive modeling techniques that take account of variations in blood glucose concentration within and between individuals. It describes their use in developing multivariable models in early-warning systems for hypo- and hyperglycemia; these models are more accurate than those solely reliant on glucose and insulin concentrations because they can accommodate other relevant influences like physical activity, stress and sleep.

Such factors also contribute to the accuracy of the adaptive control systems present in the artificial pancreas which is the focus of the brief, as their presence is indicated before they have an apparent effect on the glucose concentration and so can be more easily compensated. The adaptive controller is based on generalized predictive control techniques and also includes rules for changing controller parameters or structure based on the values of physiological variables. Simulation studies and clinical studies are reported to illustrate the performance of the techniques presented.

About the Author

 Ali Cinar is Professor of Chemical Engineering at the Illinois Institute of Technology.  His research concentrates on three areas: modeling, simulation and control of biomedical systems, complex adaptive agent-based systems, and supervision of process operations. His research activities focus on the development of theory, methods, and tools to use in these application areas. He is a Fellow of the AIChE and the author of books on batch fermentation and chemical process performance evaluation.    

Kamuran Turksoy is a postdoctoral researcher in biomedical engineering at IIT.  His research focuses on the development of hypoglycemia early alarm systems, multivariable adaptive control systems and process monitoring, performance assessment, and fault diagnosis techniques for risk mitigation in artificial pancreas systems. He has developed software to implement the methods and algorithms developed, and tested them in simulation and clinical studies.