Hardware design for blood glucose control based on the Sorensen diabetic patient model using a robust evolving cloud-based controller

Diabetes Mellitus (DM) is the most hazardous public health challenge requiring engineering study to prevent disease complications. In this paper, a Sorensen-based diabetic model is presented in which the insulin-glucose process of a Type 1 patient is maintained by considering other factors such as physical characteristics and changes in mental aspects of the diabetic patient. The purpose of the research is to include a non-linear model of a patient with diabetes who is affected by stress, meals, exercise, and Insulin Sensitivity (IS), and a suitable RECCo controller is designed as a notable recent innovation that implements the concept of ANYA fuzzy rule-based system, which is an online adaptive type of controller that is used in this research work with an uncertainty case of the condition, where the blood glucose must be regulated. To ensure the performance of the proposed controller, a simple insulin pump is designed in a practical case, and a hardware experiment is conducted. The result of the hardware is analyzed and shows the success of the implementation of the controller in blood glucose regulation, thereby preventing complications such as hypoglycemia and hyperglycemia. The comparison analysis of RECCo was performed with other types of controllers, such as MPC and MRAC. The accuracy of the model was validated using the N-BEATS algorithm with a data-set collected from the simulated model, which is around 98%.