Glucose–Insulin Interaction System for Type 1 Diabetes Mellitus Patients Based on an Adaptive Aquila Optimization–Tilt Acceleration
DOI:
https://doi.org/10.63746/njtd.v23i3.3565Keywords:
glucose, insulin, artificial pancreas, optimization, controllerAbstract
ype-I Diabetes Mellitus (TIDM) requires continuous external insulin regulation, and artificial pancreas (AP) systems offer closed-loop control through glucose sensing, control computation, and micro-insulin dispensing. Glucose-insulin dynamics are nonlinear, time-varying, patient-specific, and affected by delays, meal/exercise disturbances, sensor noise, and parameter uncertainty. Conventional PID and advanced controllers often show limited robustness, slow response, and poor adaptability. This study designs an Aquila Optimization–Tilt Acceleration Derivative with Filter (AO-TADF) controller to regulate insulin delivery and maintain Blood Glucose (BG) within 70–120 mg/dl. A multi-compartment Lehmann glucose–insulin patient model is implemented in SIMULINK. Meal and exercise disturbances are included. Linearization at 81 mg/dl and basal insulin 22.3 mU/min yields state-space matrices. TADF combines tilt, acceleration, and derivative actions with pre-filters, gains, and shaping coefficients. The Aquila Optimizer, mimicking high soaring, contour gliding, low-altitude flight, and prey capture, tunes parameters by minimizing integral time absolute error. The closed-loop AP includes glucose sensing, the AO-TADF controller, and a micro-insulin dispenser. Performance is tested under a 60 g meal at 600 min, exercise at 1300 min, hepatic/peripheral insulin sensitivity variations, and noise. BG stabilized at 81–83.3 mg/dl with no renal glucose excretion. This controller achieved 230 min settling time, 58.2 mU/min insulin infusion, 3.9 mg/dl peak overshoot, 1.0 mg/dl undershoot, zero steady-state error, mean BG 84 mg/dl, and standard deviation 1.5, outperforming PID, fuzzy, sliding mode, LQG, and H? controllers. The AO-TADF controller provides robust, stable, and accurate glycemic regulation, and promising for practical AP implementation, with future validation using clinical simulators and real-time embedded platforms
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