Tobias M. Wolff

Tobias M. Wolff, Isabelle Krauss, Victor G. Lopez et al. · tsinghua

In this work, we introduce a sample- and data-based moving horizon estimation framework for linear systems. We perform state estimation in a sample-based fashion in the sense that we assume to have only few, irregular output measurements available. This setting is encountered in applications where measuring is expensive or time-consuming. Furthermore, the state estimation framework does not rely on a standard mathematical model, but on an implicit system representation based on measured data. We prove sample-based practical robust exponential stability of the proposed estimator under mild assumptions. Furthermore, we apply the proposed scheme to estimate the states of a gastrointestinal tract absorption system.

Seth Siriya, Tobias M. Wolff, Isabelle Krauss et al.

Model-based control techniques have recently been investigated for the recommendation of medication dosages to address thyroid diseases. These techniques often rely on knowledge of internal hormone concentrations that cannot be measured from blood samples. Moreover, the measurable concentrations are typically only obtainable at irregular sampling times. In this work, we empirically verify a notion of sample-based detectability that accounts for irregular sampling of the measurable concentrations on two pituitary-thyroid loop models representing patients with hypo- and hyperthyroidism, respectively, and include the internal concentrations as states. We then implement sample-based moving horizon estimation for the models, and test its performance on virtual patients across a range of sampling schemes. Our study shows robust stability of the estimator across all scenarios, and that more frequent sampling leads to less estimation error in the presence of model uncertainty and misreported dosages.