Artificial pancreas control using optimized fuzzy logic based genetic algorithm

The aim of this paper is to develop an artificial pancreas that can automate the process of monitoring blood glucose levels and administering insulin to diabetic patients. The device incorporates a fuzzy controller that is optimized through a genetic algorithm and designed using MATLAB. The system comprises three key components: a continuous glucose monitoring (CGM) system, an insulin pump, and the fuzzy controller. The CGM measures blood glucose levels in real-time, and the insulin pump administers insulin doses to maintain blood glucose levels within a specific range. The fuzzy controller adjusts the insulin delivery rate based on the patient's blood glucose levels and their target range. To enhance the system's performance, a genetic algorithm is used to fine-tune the parameters of the fuzzy controller, seeking the optimal set of parameters that minimize the difference between the patient's blood glucose levels and the desired target range. The system is implemented in MATLAB, and simulation results indicate its effectiveness in maintaining blood glucose levels within the desired target range, reducing the risk of hypoglycemia and hyperglycemia. In summary, the proposed artificial pancreas system provides an effective automated solution for monitoring blood glucose levels and administering insulin to diabetic patients, with the fuzzy controller and genetic algorithm optimization enhancing the system's performance.