Validation in diabetic rats of a fully automated insulin delivery system based on impulsive offset-free MPC control

The development of an impulsive automated insulin delivery system (i-AiDS) for type 1 diabetes mellitus aims to provide real-time blood glucose regulation with minimal human intervention. This study presents the validation of an offset-free impulsive zone model predictive control strategy designed to cope with external disturbances such as meal intake and plant-model mismatch in a diabetic rat model. Fourteen male Wistar rats induced diabetes with streptozotocin were monitored using an continuous glucose monitoring and regulated by delivering insulin with a customized low-cost pump. After acquiring diabetes condition, the procedure for installing the devices in the rat is carried out. During the first day, manual insulin injections are made by the pump, the glucose response is recorded by the interface and an off-line parametric estimation is executed. Based on the parameters found, simulations are used for the first tuning of the controller and the estimator. During the second day, the parameters of the model and the control are tested and adjusted. Finally, on the third day, a 72-hour test of the impulsive begins in full autonomous mode. Results showed that the controller achieved an average of 83.4% of the time within the target range of 80-180 mg/dL, with no severe hypoglycemic or hyperglycemic events. The median absolute relative difference between model predictions and actual sensor data was 24.66%, indicating the presence of plant-model mismatch that was effectively handled by the controller. Peak hyperglycemic events reached 320 mg/dL, but were regulated within 50 minutes, while mild hypoglycemic events occurred in 3.62 ± 1.8 cases per subject. The study demonstrates the efficacy of the controller in managing unannounced carbohydrate intake and physiological disturbances in a real-world preclinical environment. These findings provide a foundation for future clinical trials, emphasizing the importance of in vivo validation of control strategies to refine the i-AiDS for human use. Improvements in model accuracy and dynamic parameter tuning could further enhance performance, particularly in longer experimental periods.