Kalman Filtering of Discrete LPV Diabetes Mellitus Model for Control Purposes

In this study we investigate the application of state estimation by using linear parameter varying (LPV) based extended Kalman filtering (EKF) concerning type 1 diabetes mellitus. We consider the widely used Cambridge model which has high complexity and several unfavorable properties from control engineering perspective. The applied model is transformed and scheduling variables are selected to keep the applicability of the selected complete LPV discretization method. We investigate two LPV models which are selected based on different benefits. Two EKFs are designed and the operation of them are compared to the original nonlinear model. According to the results both EKF can be applied despite their essential differences. Moreover, there is no significant deviation between their performance. Namely, both designed discrete LPV kind EKF estimated the states of the reference system with applicable error.

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