In 2016, diabetes was the world's sixth largest cause of mortality. Diabetes is a metabolic disorder marked by a hyperglycemic state in which blood glucose levels are abnormally high, commonly after eating. Diabetes is divided into 2 types, namely type-1 diabetes and type-2 diabetes, which is discussed in this research is type-1. Type-1 diabetes is characterized by insufficient insulin production and requires daily insulin administration, necessitating the use of a controller that can automatically handle the insulin dose in order to avoid complications caused by increased glucose levels after eating. The controller used is PID, it is vital to build a PID controller capable of providing a good response while ensuring system stability and accuracy. The PID tuning approach employed is Particle Swarm Optimization (PSO), which is an algorithm that mimics natural processes. It's connected to swarm theory, bird flocking, and fishing schooling. The PSO was designed to simulate birds searching for food. The artificial pancreas control system in this work was designed using the Bergman Minimal Model (BMM), which was applied to a variety of conditions. PID parameters generated by the PSO method are Kp = -52,3119, Ki = -1,8070, Kd = -10,1027 with Integral Time Absolute Error (ITAE1) 50.702, and ITAE2 36.360, Overshoot 0.52% and Settling time 1,89 second. These results are the most optimal results in this research and can be implemented in an artificial pancreas control system and can maintain glucose levels between 68 mg/dL- 104 mg/dL and tend to maintain to the set point of 76,2159 mg/dL.
|AIP Conference Proceedings
|Published - 16 Aug 2023
|11th International Conference on Theoretical and Applied Physics: The Spirit of Research and Collaboration Facing the COVID-19 Pandemic, ICTAP 2021 - Virtual, Online, Indonesia
Duration: 27 Oct 2021 → 28 Oct 2021
- Artificial Pancreas
- PID Controller
- Particle Swarm Optimization (PSO)