Self-tuned Blood Glucose (BG) Level Adjustment in Type-I Diabetes Patient based on Adaptive Control Approach - Akshaya Kumar Patra; Smitta Ranjan Dutta; Anuja Nanda (Current Science, Engineering and Technology, 2026)
Introduction/Objective: Type-I Diabetes Mellitus (TIDM) is characterized by autoimmune destruction of pancreatic β-cells, resulting in absolute insulin deficiency and impaired glucose regulation. Maintaining normoglycemia is challenging due to the nonlinear and time-varying nature of Glucose–Insulin (GI) dynamics, meal disturbances, and physiological uncertainties. This study proposes a Grasshopper Optimization-based Tilt–Acceleration– Derivative with Filter (GO-TADF) controller for adaptive insulin delivery in an Artificial Pancreas (AP) framework. The objective is to improve Blood Glucose (BG) regulation, robustness, and disturbance rejection. The investigation is conducted entirely through simulation using a validated physiological model; therefore, clinical and real-patient data validation remains an important direction for future research. Methods: A nonlinear multi-organ GI model incorporating hepatic balance, gut absorption, renal excretion, and insulin kinetics is employed. The TADF controller regulates insulin infusion, while the Grasshopper Optimization Algorithm (GOA) tunes controller parameters using the Integral Time Absolute Error (ITAE) criterion. Simulations are conducted under meal disturbances, parameter variations, and sensor noise to evaluate performance. Results: The GO-TADF controller maintains blood glucose near the normoglycemic range with faster convergence, reduced overshoot/ undershoot, and stable insulin infusion. Renal glucose excretion becomes negligible at steady state. Comparative analysis shows superior robustness, accuracy, and noise rejection over PID, Linear Quadratic Gaussian (LQG), Sliding Mode (SM), Model Predictive Control (MPC), and related controllers. Discussion: The proposed GO-TADF controller demonstrated superior BG regulation in TIDM patients by effectively handling nonlinear dynamics, meal disturbances, and physiological uncertainties. Compared with existing controllers, it achieved faster settling time, lower overshoot, reduced insulin consumption, and improved robustness, making it a promising solution for AP applications Conclusions: The proposed GO-TADF controller effectively regulates BG in TIDM patients by providing robust, adaptive, and stable insulin delivery. Simulation results demonstrate improved glucose control, reduced insulin consumption, and superior disturbance rejection, highlighting its potential for artificial pancreas applications.