Banca de DEFESA: JOÃO LUCAS CORREIA BARBOSA DE FARIAS

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : JOÃO LUCAS CORREIA BARBOSA DE FARIAS
DATE: 02/10/2025
TIME: 08:30
LOCAL: Remoto - Online
TITLE:

Control of an Automated Insulin Delivery System using Artificial Intelligence

KEY WORDS:

automated insulin delivery system, nonlinear control, feedback linearization, machine learning, reinforcement learning, recurrent neural networks.

PAGES: 112
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUMMARY:

Type 1 Diabetes Mellitus is a disease that affects millions of people worldwide. Recently, devices that automatically regulate blood glucose concentration in diabetic patients were developed. The Automated Insulin Delivery (AID) system can provide a life with higher quality, autonomy, and comfort for patients. This work aims to design an intelligent nonlinear controller with an estimator developed with Artificial Intelligence (AI) algorithms for an AID system. An extensive review on diabetes is conducted introducing its definition, physiological context, diagnosis, global impact and available state of the art therapies. Furthermore, a literature review is carried out presenting the types of AID systems, current challenges, virtual patient models, control strategies and AI algorithms that shape modern designs. The OHSU virtual patient model, used to simulate the virtual patient population in this work, is introduced along with its mathematical formulation, which incorporates physical activity to the insulin-glucose dynamics. The proposed controller combines Feedback Linearization with an intelligent neural network-based estimator to compensate for model uncertainties, external disturbances and time delays inherent of the biological system in question. It incorporates a Reinforcement Learning algorithm, Q-learning, to optimize neural network learning rate across a diverse virtual patient population while minimizing hypoglycemic events. Results show the controller was able to safely and effectively regulate glycemia with average Time in Range (TIR) of 81% and Coefficient of Variation (CV) of 35% on a diverse training population of 10 subjects, and average TIR of 76% and CV of 34% for a different unseen validation population of 5 subjects. In order to avoid poor control during the network’s initial learning period, offline network pre-training is proposed and tested on the validation population. Severe hyperglycemic events in the early days of simulation were completely avoided with the added benefit of a slight improvement in TIR (76% vs. 78%), indicating a clear path on how to introduce the controller to real-life subjects. Overall, the proposed controller was able to provide safe and effective glycemic control under exercising across a diverse virtual patient population without the need for user input on meal intakes or physical activities.

COMMITTEE MEMBERS:
Presidente - 1445637 - WALLACE MOREIRA BESSA
Interno - 1328152 - CARLOS EDUARDO TRABUCO DOREA
Externo ao Programa - 1338796 - PHILIPPE EDUARDO DE MEDEIROS - UFRNExterno à Instituição - ARTHUR HIRATA BERTACHI - UTFPR
Externo à Instituição - AMERICO BARBOSA DA CUNHA JUNIOR - LNCC