Closed-Loop Control of Blood Glucose Levels in Simulated Type-1 Diabetes Patients With a New Type of I-PD Controller

Abstract

Accurate Blood Glucose (BG) control in Type 1 Diabetes Mellitus (T1DM) remains difficult due to the nonlinear and time-delayed characteristics of the glucose&#x2013;insulin system. In this study, new type Integral-PD (I-PD) control method, which provides better operating performance in time-delayed systems, will be discussed and its performance in BG control of T1DM patients will be examined. This study evaluates four different model-based control schemes as I-PD, Proportional-Integral-PD (PI-PD), Proportional-Derivative (PD) and Self-Tuning PD (STPD), to compare their performances for closed-loop BG regulation using the UVA/Padova T1DM Simulator. While linearized patient models were derived through System Identification (SI) solely for controller synthesis, the performance and robustness of the proposed algorithms were rigorously validated using the original non-linear dynamics of the UVA/Padova simulator across a 48-hour meal scenario involving 30 virtual subjects. In glucose control applications, linearized patient models were obtained through System Identification (SI) in MATLAB and tested over a 48-hour meal scenario for 30 virtual subjects across three age groups. Results show that I-PD and PI-PD controllers achieved superior regulation with Time in Range (TIR) values of 76.2% and 76.4% in adults, respectively, outperforming PD (65.2%) and STPD (59.4%). In adolescents, TIR remained above 67 %, demonstrating strong robustness and low variability. Pediatric simulations yielded lower stability (TIR <inline-formula> <tex-math notation="LaTeX">$\approx ~51$ </tex-math></inline-formula>&#x2013;53 %) due to rapid metabolic dynamics. Overall, the findings confirm that integral-assisted PD controllers achieve stable, accurate, and safe BG regulation in closed-loop operation. The proposed control structures demonstrated strong robustness, low variability, and effective mitigation of both hypoglycemia and hyperglycemia, indicating their potential suitability for future integration into artificial pancreas systems.