Researches on artificial pancreas are extremely popular nowadays, because type 1 diabetes is a frequently occurring problem world wide. Type 1 diabetes is incurable and strongly affects life quality. Ultimately this condition can be fatal.
A key aspect of the artificial pancreas research is to accurately describe the human glycemic and metabolic processes with a usually non-linear model. We would have the opportunity to create virtual patients for the test environment by identify the parameters of the non-linear model. Furthermore, model-based control methods require a reliable model. Accurate modeling could speed up the artificial pancreas development. The non-linear model is inaccurate, loaded with noises and disturbances, so the simultaneous estimation of the state variables and model parameters could significantly improve the effectiveness of identification. I dealt with estimating the state variables in my thesis.
I started the state estimation with implementing a Kalman filter first, then I extended it to nonlinear systems as an Extended Kalman filter. The Extended Kalman filters disadvantage is the error coming from the linearization. To avoid this problem, I performed the state estimation with sigma point filters.
After the estimation of state variables I supplemented the filter algorithm to make it capable to estimate the model parameters.
The results were evaluated both on simulated and real clinical data sets.