Abstract
Based on intervals bounding the uncertain initial conditions, inputs, model parameters and measurements, interval observers provide guaranteed intervals for the state trajectory. However, most of the published studies focus on methods relying on continuous-time on-line measurements, or at least, with relatively fast sampling. In this study, interval state estimation methods are proposed in the situation, quite common in biological systems, where measurements are only available at discrete, and possible rare, times. The attention is focused on defining predictors preserving the boundedness of the state variables between two measurement times assuming bounded uncertainties. The methods are tested with experimental data from continuous cultures of the green algae Dunaliella tertiolecta.
Published Version
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