Buch, Englisch, 162 Seiten, Format (B × H): 235 mm x 192 mm, Gewicht: 342 g
Buch, Englisch, 162 Seiten, Format (B × H): 235 mm x 192 mm, Gewicht: 342 g
ISBN: 978-0-323-90776-7
Verlag: Elsevier Science & Technology
Nonlinear Control for Blood Glucose Regulation of Diabetic Patients: An LMI-Based Approach exposes readers to the various existing mathematical models that define the dynamics of glucose-insulin for Type 1 diabetes patients. After providing insights into the mathematical model of patients, the authors discuss the need and emergence of new control techniques that can lead to further development of an artificial pancreas. The book presents various nonlinear control techniques to address the challenges that Type 1 diabetic patients face in maintaining their blood glucose level in the safe range (70-180 mg/dl).
The closed-loop solution provided by the artificial pancreas depends mainly on the effectiveness of the control algorithm, which acts as the brain of the system. APS control algorithms require a mathematical model of the gluco-regulatory system of the T1D patients for their design. Since the gluco-regulatory system is inherently nonlinear and largely affected by external disturbances and parametric uncertainty, developing an accurate model is very difficult.
Zielgruppe
<p>Students, educators, and researchers in biomedical engineering and control engineering, and electrical engineering, as well as researchers and diabetic clinicians in labs involved in the development of improved insulin therapy as well as the Artificial Pancreas system </p> <p>Can be used for graduate-level courses in biomedical control</p>
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Section 1: Introduction
1. The History, Present & Future progression of Artificial Pancreas
2. Biomedical Control & its importance in Artificial Pancreas
3. A brief discussion in Nonlinear Control Tools
Section 2: Type 1 Diabetes: Control Oriented Modelling
4. A review on the existing Artificial pancreas Models
5. Developing and validating Nonlinear Models based on Input-Output data
Section 3: State Estimation via Robust Nonlinear Observers
6. Mathematical formulation of Robust Nonlinear Observers
7. State Estimation
Section 4: Design of Robust Nonlinear Control Techniques
8. Design of Nonlinear Control Technique based on Feedback Linearization
9. Design of Robust LMI based Control Techniques
10. Conclusions
Section 5: Proposed Architecture for In-Silico Artificial Pancreas
11. Sensors and Actuators
12. Integrated (in-silico) Model of Artificial Pancreas
