State estimation of physiological systems

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Dr. Jobbágy Ákos Andor
Department of Measurement and Information Systems

This work is focused on discrete state estimation of physiological systems (type 1 diabetes and tumor growth) based on Linear Parameter Varying (LPV) framework. LPV techniques are very useful frameworks which allow the application of linear controller, observer and estimator design. Control theory is still facing considerable difficulties in the field of physiological systems, in particular considering the non-linearity of the models, time-delays, parameter uncertainties, low sampling frequencies and noises. These obstacles may overcome by the utilisation of Kalman filtering algorithms. First part of my thesis work summarizes the physiological background of diabetes mellitus and tumor growth, presents corresponding models, their possible discretization methods and a comparison study of two alternative LPV based discrete Kalman filter implementations. In the second part I investigated the possibility of combined state and parameter estimation with Joint and with Dual Kalman Filters. The performance of the estimation in case of diabetes is evaluated in an open-loop system, although in case of tumor growth the filters are embedded in a closed-loop control system.


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