Control and modeling techniques in biomedical engineering: the artificial pancreas for patients with type 1 diabetes

This thesis presents different control strategies, for the closed-loop artificial pancreas, which are based on Model Predictive Control (MPC) and Sliding Mode Control (SMC). Multiple MPC with linear models and gain scheduling, and SMC with linear and nonlinear models, have been developed. The proposed control strategies combine more than one linear/nonlinear control and modeling approaches in one structure. The main idea behind such combined approaches is to make use of the virtues of each approach while reducing the effects of their drawbacks. The control strategies have been tested and validated in simulations (in-silico validation). For the in-silico testing, two mathematical models have been used, simulating patients with Type 1 Diabetes Mellitus. The control strategies are tested in different conditions, such as the presence of meal disturbance and patient variabilityEsta tesis presenta diferentes estrategias de control para el páncreas artificial, que se basan en control predictivo basado en modelo (MPC) y el control por modo deslizante (SMC). Múltiples MPC con modelos lineales y planificación de ganancia, y SMC con modelos lineales y no lineales, se han desarrollado. Las estrategias de control propuestas combinan más de un método (lineal y/o no lineal) de control y modelado en cada estructura. La idea principal detrás de estos enfoques combinados es hacer uso de las virtudes de cada enfoque al tiempo que se reducen los efectos de sus desventajas. Las estrategias de control han sido probadas y validadas en simulaciones (validación in-silico). Para los ensayos in-silico, dos modelos matemáticos se han utilizado, simulando los pacientes con diabetes mellitus tipo 1. Las estrategias de control se ensayan en diferentes condiciones, tales como la presencia de perturbación por ingesta de comida, y variabilidad de pacient

Smith Predictor Sliding Mode Closed-loop Glucose Controller in Type 1 Diabetes

Type 1 diabetic patients depend on external insulin delivery to keep their blood glucose within near-normal ranges. In this work, two robust closed-loop controllers for blood glucose regulation are developed to prevent the life-threatening hypoglycemia, as well as to avoid extended hyperglycemia. The proposed controllers are designed by using the sliding mode control technique in a Smith predictor structure. To improve meal disturbance rejection, a simple feedforward controller is added to inject meal-time insulin bolus. Simulations scenarios were used to test the controllers, and showed the controllers ability to maintain the glucose levels within the safe limits in the presence of errors in measurements, modeling and meal estimatio

A Gain Scheduling Model Predictive Controller for Blood Glucose Control in Type 1 Diabetes

This paper presents a control strategy for blood glucose(BG) level regulation in type 1 diabetic patients. To design the controller, model-based predictive control scheme has been applied to a newly developed diabetic patient model. The controller is provided with a feedforward loop to improve meal compensation, a gain-scheduling scheme to account for different BG levels, and an asymmetric cost function to reduce hypoglycemic risk. A simulation environment that has been approved for testing of artificial pancreas control algorithms has been used to test the controller. The simulation results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against model–patient mismatch and errors in meal estimatio