arXiv subjects

Isabelle Krauss

Publications and source records attributed to Isabelle Krauss.

9 recordsLinked to original sources

arXiv2026

Sample-based detectability and moving horizon state estimation of continuous-time systems

In this paper we propose a detectability condition for nonlinear continuous-time systems with irregular/infrequent output measurements, namely a sample-based version of incremental integral input/output-to-state stability (i-iIOSS). We provide a sufficient condition for an i-iIOSS system to be sample-based i-iIOSS. This condition is also exploited to analyze the relationship between sample-based i-iIOSS and sample-based observability for linear systems, such that previously established sampling strategies for linear systems can be used to guarantee sample-based i-iIOSS. Furthermore, we present a sample-based moving horizon estimation scheme, for which robust stability can be shown. Finally, we illustrate the applicability of the proposed estimation scheme through a biomedical simulation example.

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arXiv2025

Data-based Moving Horizon Estimation under Irregularly Measured Data

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.

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arXiv2025

Estimating Hormone Concentrations in the Pituitary-Thyroid Feedback Loop from Irregularly Sampled Measurements

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.

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arXiv2025

Sample-based Moving Horizon Estimation

In this paper, we propose a sample-based moving horizon estimation (MHE) scheme for general nonlinear systems to estimate the current system state using irregularly and/or infrequently available measurements. The cost function of the MHE optimization problem is suitably designed to accommodate these irregular output sequences. We also establish that, under a suitable sample-based detectability condition known as sample-based incremental input/output-to-state stability (i-IOSS), the proposed sample-based MHE achieves robust global exponential stability (RGES). Additionally, for the case of linear systems, we draw connections between sample-based observability and sample-based i-IOSS. This demonstrates that previously established conditions for linear systems to be sample-based observable can be utilized to verify or design sampling strategies that satisfy the conditions to guarantee RGES of the sample-based MHE. Finally, the effectiveness of the proposed sample-based MHE is illustrated through a simulation example.

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arXiv2025

Robust stability of event-triggered nonlinear moving horizon estimation

In this work, we propose an event-triggered moving horizon estimation (ET-MHE) scheme for the remote state estimation of general nonlinear systems. In the presented method, whenever an event is triggered, a single measurement is transmitted and the nonlinear MHE optimization problem is subsequently solved. If no event is triggered, the current state estimate is updated using an open-loop prediction based on the system dynamics. Moreover, we introduce a novel event-triggering rule under which we demonstrate robust global exponential stability of the ET-MHE scheme, assuming a suitable detectability condition is met. In addition, we show that with the adoption of a varying horizon length, a tighter bound on the estimation error can be achieved. Finally, we validate the effectiveness of the proposed method through two illustrative examples.

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arXiv2025

On sample-based functional observability of linear systems

Sample-based observability characterizes the ability to reconstruct the internal state of a dynamical system by using limited output information, i.e., when measurements are only infrequently and/or irregularly available. In this work, we investigate the concept of functional observability, which refers to the ability to infer a function of the system state from the outputs, within a samplebased framework. Here, we give necessary and sufficient conditions for a system to be sample-based functionally observable, and formulate conditions on the sampling schemes such that these are satisfied. Furthermore, we provide a numerical example, where we demonstrate the applicability of the obtained results.

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arXiv2024

Event-triggered moving horizon estimation for nonlinear systems

This work proposes an event-triggered moving horizon estimation (ET-MHE) scheme for general nonlinear systems. The key components of the proposed scheme are a novel event-triggering mechanism (ETM) and the suitable design of the MHE cost function. The main characteristic of our method is that the MHE's nonlinear optimization problem is only solved when the ETM triggers the transmission of measured data to the remote state estimator. If no event occurs, then the current state estimate results from an open-loop prediction using the system dynamics. Furthermore, we show robust global exponential stability of the ET-MHE under a suitable detectability condition. Finally, we illustrate the applicability of the proposed method in terms of a nonlinear benchmark example, where we achieved similar estimation performance compared to standard MHE using 86% less computational resources.

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arXiv2023

Sample-based nonlinear detectability for discrete-time systems

This paper introduces two sample-based formulations of incremental input/output-to-state stability (i-IOSS), a suitable detectability notion for general nonlinear systems. In this work we consider the case of limited output information, i.e., measurements are only infrequently and/or irregularly available. The output-dependent term of the sample-based i-IOSS bound is properly modified to yield a characterization for detectability in presence of incomplete output sequences. We provide both a non-timediscounted and a time-discounted formulation of samplebased i-IOSS. Furthermore, conditions for an i-IOSS system to be also sample-based i-IOSS are given and the relation between the two formulations of sample-based i-IOSS is shown.

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arXiv2023

Sample-based observability of linear discrete-time systems

In this work, sample-based observability of linear discrete-time systems is studied. That is, we consider the case where the system output measurements are not available at every time instance. It is shown that some discrete-time systems exhibit particular behaviors that lead to pathological sampling. Depending on the characteristics of the system, different sampling schemes are developed that allow the system state to be reconstructed.

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