There has been much advancement in the field of medicine over the course of human history, but especially in the last century. Many medicinal theories have come and gone with more accurate and up-to-date research. Today testing and validation of research is often done with computers and mathematical modeling. One of the common fields in medicine which uses similar computer simulation is insulin replacement therapy for diabetic patients. A diabetic patient lacks the capability of naturally producing insulin the human body needs to regulate blood sugar. The pancreas is responsible for producing insulin to counteract the constant fluctuations in blood sugar. Many factors such as eating, exercising and even sleeping will cause blood sugar levels to rise and fall. Without regular doses of insulin to regulate blood sugar, diabetic patients can experience health complications. Many methods of insulin replacement for diabetic patients have been used throughout the history of treating diabetes. The most innovative and current methods of insulin replacement today stems from the invention of the artificial pancreas. This thesis focuses on the design of a controller for the artificial pancreas with the goal of maximizing its effectiveness. A mathematical metabolic model of the human body, which is validated by the FDA to be used in nonhuman virtual trials, is used. With this model, the user is able to define a wide variety of parameters and most importantly design a controller to make the closed loop system work as effectively as possible. There are many methods that have been used to design a controller, For this thesis, the focus will be evaluating the use of Fuzzy Logic at the heart of the controller for the artificial pancreas. It is shown that with the combination of the mathematical model and the Fuzzy Logic controller, improved performance was achieved. Various implementations of Fuzzy Logic control are detailed and compared to other methods developed by other researchers. The results show Fuzzy Logic has the potential to be a very effective method to use for the control of an artificial pancreas.

This book tackles the problem of overshoot and undershoot in blood glucose levels caused by delay in the effects of carbohydrate consumption and insulin administration. The ideas presented here will be very important in maintaining the welfare of insulin-dependent diabetics and avoiding the damaging effects of unpredicted swings in blood glucose – accurate prediction enables the implementation of counter-measures. The glucose prediction algorithms described are also a key and critical ingredient of automated insulin delivery systems, the so-called “artificial pancreas”. The authors address the topic of blood-glucose prediction from medical, scientific and technological points of view. Simulation studies are utilized for complementary analysis but the primary focus of this book is on real applications, using clinical data from diabetic subjects. The text details the current state of the art by surveying prediction algorithms, and then moves beyond it with the most recent advances in data-based modeling of glucose metabolism. The topic of performance evaluation is discussed and the relationship of clinical and technological needs and goals examined with regard to their implications for medical devices employing prediction algorithms. Practical and theoretical questions associated with such devices and their solutions are highlighted. This book shows researchers interested in biomedical device technology and control researchers working with predictive algorithms how incorporation of predictive algorithms into the next generation of portable glucose measurement can make treatment of diabetes safer and more efficient.

This book presents closed-loop blood glucose control in a simple manner, which includes the hardware and "software" components that make up the control system. It provides examples on how mathematical models are formulated as well as the control algorithms that stem from mathematical exercises. The book also describes the basic physiology of blood glucose regulation during fasting and meal from a functional level.

This book emphasizes on a Field Programmable Gate Array (FPGA) Implementation of Fuzzy PD and PID Controller for biomedical application. A novel approach aims to identify and design a simple robust Fuzzy (PD and PID) Controller system with minimum number of fuzzy rules to deliver insulin from pumps as a single injection process for the diabetic patients. This controller is an automatic feedback control system in which the patient's blood glucose level is monitored from non-invasive Photo plethysmogram of a pulse oximeter (Photoglucometer) and insulin is infused with the change in the glucose level of the diabetic patient. This work was carried out in three phases like phase -I VLSI Design and simulation of Fuzzy PD controller were performed and analyzed. From the VLSI Simulation results FPGA Model was synthesized in the phase-II. During phase -III performance of the fuzzy PD Controller was evaluated. VLSI fuzzy system was simulated and analyzed then FPGA module was synthesized and implemented using VHDL.A part of this work was sponsored by IEEE Inc., (U.S.A) under scheme IEEE Student Enterprise Award 2007-2008 title FPGA Model of Fuzzy PD Controller for Insulin Pumps In Diabetes.

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. Presents control-oriented modeling of the gluco-regulatory system of Type 1 diabetic patients using input-output data Demonstrates the design of a robust insulin delivery mechanism utilizing state estimation information with parametric uncertainties and exogenous disturbance in the framework of Linear Matrix Inequality (LMI) Introduces readers to the relevance and effectiveness of powerful nonlinear controllers for the Artificial Pancreas Provides the first book on LMI-based nonlinear control techniques for the Artificial Pancreas

Biomedical Instrumentation is an exciting field to discover and explore. Diabetes mellitus is one of the critical research areas which has been addressed for decades. The development of the constant glucose measuring devices and insulin pumps give an expectation for better well-being. The pancreas in the body plays a dynamic part in regulating the glucose level in blood. The function of the pancreas to produce insulin enzymes. The deficiency of the pancreas causes to rise in blood glucose level. Many research scientists are working on diagnosing and treating this diabetes mellitus disease. The mathematical model adopted in this work represents the dynamics of glucose and insulin. The name of model is extended Bergman Minimal Model. With this model, a closed loop feedback control system of blood glucose-insulin is developed to normalize the glucose level in blood. The developed system is implemented and analyzed with a software tool called MATLAB. The PID control and Fuzzy control methods are designed for exogenous insulin infusion pumps. These control methods are applied to the developed system to maintain the blood glucose level in desired range. Simulations shows that both designed control methods can keep the blood glucose level in desired range. However, the PID control method able to achieve the setpoint while the fuzzy control is never able to reach the setpoint value. With the PID control method, the output is oscillatory in nature to give blood glucose value before coming to the setpoint value. The fuzzy controller gives small overshoot in output but within the normal range.

This book presents the select proceedings of the International Conference on Industrial and Manufacturing Systems (CIMS 2020). It presents the current scenarios and future advancements in the domain of industrial engineering under context of optimum value. Various topics covered include optimisation and its applicability in the various areas of industrial engineering like selection of designing parameters and, decisions related to conditions of optimum process/operation parameters, facilities planning and management, transportation and supply chain management, quality engineering, reliability and maintenance, system optimization, product design and development, human factors and ergonomics, project management, service system and service management, waste management, sustainable manufacturing and operations, systems design, lean manufacturing, and performance measurement. This book will be useful for the students, researchers and professionals working in the area of industrial and production engineering.