Control of Type 1 Diabetes Mellitus using Particle Swarm Optimization driven Receding Horizon Controller

Abstract Receding Horizon Control (RHC), also known as Model Predictive Control (MPC) is one of the most intensively researched areas of control algorithms applied in the artificial pancreas concept. Nevertheless, MPC algorithms have not yet been implemented in commercially available insulin pumps, mainly due to their high computational demand, their less robust nature, and their instability on account of model’s uncertainty. In this paper, we present a robust adjustable RHC. The proposed RHC controller was tested under known food inputs by applying a high degree of parameter uncertainty to the virtual patient implemented in the controller to test the robustness of the architecture. A particle swarm optimization method was applied to tune the controller. The so-called identifiable virtual patient (IVP) model was used in the tests, supplemented with food absorption and continuous glucose monitoring sensor model. The implementation was performed in Julia. The results showed that the proposed RHC is sufficiently robust under high food intake and parameter uncertainty.