| Abstract: | The error of single step-ahead output prediction is the information traditionally used to correct the state estimate while
exploiting the new measurement of the system output. However, its dynamics and statistical properties can be further studied
and exploited in other ways. It is known that in the case of suboptimal state estimation, this output prediction error forms a
correlated sequence, hence it can be effectively predicted in real time. Such a suboptimal scenario is typical in applications
where the process noise model is not known or it is uncertain. Therefore, the paper deals with the problems of analytical
and empirical modeling, identification, and prediction of the output error of the suboptimal state estimator for the sake of
improving the output prediction accuracy and ultimately the performance of the model predictive control. The improvements
are validated on an empirical model of type 1 diabetes within an in-silico experiment focused on glycemia prediction and
implementation of the MPC-based artificial pancreas. |
