Design of Robust Evolving Cloud-Based Controller for Type 1 Diabetic Patients Using n-Beats Algorithm

Abstract

Abstract Designing and analyzing adaptive controllers to control blood glucose levels by giving insulin in the Lehman-Based Diabetic Patient Model (LBDPM) while considering diverse stochastic environments in gaining popularity is challenging task. RECCo, a notable recent innovation that implements the concept of the ANYA fuzzy rule-based system, is an online adaptive type controller that is used in this study for the application of diabetes. The simulation results show that the suggested controller is used in the model to track standard blood glucose values even in the presence of some unexpected external disturbances. The primary concern in the field of type 1 diabetes is achieving higher accuracy using a deep learning algorithm with data obtained from simulated patient models. To achieve better accuracy, validation of the model is performed using the N-BEATS algorithm. By utilizing an online parameter estimation technique, the RPME is integrated to improve the performance of the adaptive model predictive controller. The system identification technique is used to attain a transfer function that is designed further for implementation of the controller. The experimental validation of the proposed N-BEATS algorithm method is compared with other conventional machine learning algorithms. The proposed controller method attains excellent blood glucose set point tracking and the proposed algorithms give accuracy rates of 97.4% and 96% for the data obtained. It outperforms other state-of-the-art methods with an increase in the accuracy percentage compared with other Benchmark Pima Indian Diabetes Datasets (PIDD).

Keywords

• Adaptive Model Predictive Control (AMPC)
• Glucose-Insulin (GI)
• Lehman Based Diabetic Patient Model (LBDPM)
• Neural Network (NN)
• Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS)