PRINCIPAL INVESTIGATOR
POSTDOCS
ORGANISATION AND ADMINISTRATION
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 948679).
Project Overview
Summary
The goal of this project is to develop estimation and control strategies for dynamical systems where only a very limited amount of information is available. This information includes current measurements of the system as well as a mathematical model. The main motivation for considering these problems are biomedical applications, where such a small amount of available information is often inherent. Examples include hormone concentration measurements when considering thyroidal diseases (which are typically only taken every several days/weeks) or monitoring the size of a tumour. Estimating the current state of the system and devising appropriate control actions (e.g., therapy or medication in the biomedical examples) is very challenging in such applications. Achieving these objectives requires the development of novel methods and tools. Within this project, particular focus lies on the following aspects.
First, we study under which conditions is it possible to reconstruct the full internal system information using only few output measurements. Sampling strategies and suitable nonlinear state estimators are derived.
Second, state estimation and control strategies are developed for situations with only partial or no knowledge of a mathematical model of the system in question. This necessitates the design of so called data- and learning-based methods, for which desired guarantees can be given, even in case of few measurements.
Third, the developed tools are extended to large-scale systems, where estimation and control has to be achieved in a distributed fashion.
The results that have been obtained within this project
- enable estimation and control in systems with very few, sampled measurements,
- constitute a big step towards a holistic data-based systems and control theory,
- result in a new, data-driven, paradigm for the control of large-scale systems, and
- enable the design of optimal control strategies in biomedical applications.
Team
Further Information
Publications
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(2026): Numerical methods for linear time-invariant systems analysis and design in the behavioral setting, submitted
DOI: 10.15488/20620 -
(2026): On Data-based Nash Equilibria in LQ Nonzero-sum Differential Games, submitted
arXiv: 2601.11320 -
(2025): Data-based system representations from irregularly measured data, IEEE Transactions on Automatic Control, vol. 70, no. 1, pp. 143-158
DOI: 10.1109/TAC.2024.3423053
arXiv: 2307.11589 -
(2025): Sufficient Conditions for Detectability of Approximately Discretized Nonlinear Systems, 2025 European Control Conference (ECC)
DOI: 10.23919/ECC65951.2025.11186853
arXiv: 2505.17857 -
(2025): On Sample-Based Functional Observability of Linear Systems, IEEE Control Systems Letters, vol. 9, pp. 1393-1398
DOI: 10.1109/LCSYS.2025.3582512
arXiv: 2506.23744v1 -
(2025): Robust stability of event-triggered nonlinear moving horizon estimation, submitted
arXiv: 2510.04814 -
(2025): Estimating Hormone Concentrations in the Pituitary-Thyroid Feedback Loop from Irregularly Sampled Measurements, submitted
arXiv: 2512.10657 -
(2025): Data-based Moving Horizon Estimation under Irregularly Measured Data, submitted
arXiv: 2512.20259 -
(2025): Inference in Latent Force Models Using Optimal State Estimation, submitted
arXiv: 2512.20250 -
(2025): Data-driven stabilization of nonlinear systems via descriptor embedding, submitted
arXiv: 2511.01457 -
(2025): Gaussian Process-Based Nonlinear Moving Horizon Estimation, IEEE Transactions on Automatic Control, Vol. 70, Iss. 12, pp. 7875 - 7890
DOI: 10.1109/TAC.2025.3580033
arXiv: 2402.04665 -
(2025): Modeling and Predictive Control for the Treatment of Hyperthyroidism, 11th Vienna International Conference on Mathematical Modelling (MATHMOD 2025), IFAC-PapersOnLine, No. 1, vol. 59, pp. 235-240
DOI: 10.1016/j.ifacol.2025.03.041
arXiv: 2212.10096 -
(2025): Gaussian Processes with Noisy Regression Inputs for Dynamical Systems, 2025 American Control Conference (ACC), pp. 160-165
DOI: 10.23919/ACC63710.2025.11107496
arXiv: 2408.08834 -
(2025): Notes on data-driven output-feedback control of linear MIMO systems, IEEE Transactions on Automatic Control, vol. 70, iss. 9, pp. 6143-6150
DOI: 10.1109/TAC.2025.3553073
arXiv: 2311.17484 -
(2025): Sample-based nonlinear detectability for discrete-time systems, IEEE Transactions on Automatic Control, vol. 70, no. 4, pp. 2422 - 2434
DOI: 10.1109/TAC.2024.3485486
arXiv: 2312.13658 -
(2024): Data-Based Control of Continuous-Time Linear Systems with Performance Specifications, submitted
arXiv: 2403.00424 -
(2024): Data-Based System Representation and Synchronization for Multiagent Systems, 2024 IEEE 63rd Conference on Decision and Control (CDC), pp. 6736-6742
DOI: 10.1109/CDC56724.2024.10886612
arXiv: 2404.13937 -
(2024): Event-triggered moving horizon estimation for nonlinear systems, Proceedings of the 2024 IEEE 63rd Conference on Decision and Control (CDC), pp. 3801-3806
DOI: 10.1109/CDC56724.2024.10886849
arXiv: 2408.06208 -
(2024): An Input-Output Continuous-Time Version of Willems’ Lemma, IEEE Control Systems Letters, vol. 8, pp. 916-921
DOI: 10.1109/LCSYS.2024.3406057
arXiv: 2405.15482 -
(2024): Identification from data with periodically missing output samples, Automatica, 2024, Vol. 169, p. 111869
DOI: 10.1016/j.automatica.2024.111869
arXiv: 10.15488/15821 -
(2024): Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems, IEEE Transactions on Automatic Control, vol. 69, iss. 8, pp. 5598-5604
DOI: 10.1109/TAC.2024.3371373
arXiv: 2210.09017 -
(2024): Sample- and computationally efficient data-driven predictive control, 2024 European Control Conference (ECC), Stockholm, Sweden. pp. 84-89
DOI: 10.23919/ECC64448.2024.10591022
arXiv: 2309.11238 -
(2024): An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems, Proceedings of the 6th Annual Learning for Dynamics & Control Conference, vol. 242, pp. 312-323 Weitere Informationen
arXiv: 2312.03451 -
(2023): An efficient off-policy reinforcement learning algorithm for the continuous-time LQR problem, Accepted for IEEE 62nd Conference on Decision and Control (CDC)
DOI: 10.1109/CDC49753.2023.10384256
arXiv: 2303.17819 -
(2023): Robust Stability of Gaussian Process Based Moving Horizon Estimation, 2023 IEEE 62nd Conference on Decision and Control (CDC), pp. 4087-4093
DOI: 10.1109/CDC49753.2023.10383304
arXiv: 2304.06530 -
(2023): Model predictive control for the prescription of antithyroid agents, Proceedings on Automation in Medical Engineering Weitere Informationen
arXiv: 2307.16690 -
(2023): Data-Based Control of Feedback Linearizable Systems, IEEE Transactions on Automatic Control, vol. 68, no. 11, pp. 7014-7021
DOI: 10.1109/TAC.2023.3249289
arXiv: 2204.01148 -
(2023): Data-driven nonlinear predictive control for feedback linearizable systems, 22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023. IFAC-PapersOnLine, Vol. 56, Issue 2, pp. 617-624
DOI: 10.1016/j.ifacol.2023.10.1636
arXiv: 2211.06339 -
(2023): Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems, IEEE Transactions on Automatic Control, vol. 68, no. 5, pp. 2922-2933
DOI: 10.1109/TAC.2023.3235967
arXiv: 2105.07761 -
(2023): On the design of persistently exciting inputs for data-driven control of linear and nonlinear systems, IEEE Control Systems Letters, vol. 7, pp. 2629 - 2634
DOI: 10.1109/LCSYS.2023.3287133
arXiv: 2303.08707 -
(2022): Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems, 2022 European Control Conference (ECC), pp. 1778-1783
DOI: 10.23919/ecc55457.2022.9838331
arXiv: 2111.04979 -
(2022): Linear tracking MPC for nonlinear systems part I: the model-based case, IEEE Trans. Automat. Control, Vol. 67, Issue 9, pp. 4390-4405
DOI: 10.1109/TAC.2022.3166872
arXiv: 2105.08560 -
(2022): Linear tracking MPC for nonlinear systems part II: the data-driven case, IEEE Trans. Automat. Control, Vol. 67, Issue 9, pp. 4406-4421
DOI: 10.1109/TAC.2022.3166851
arXiv: 2105.08567 -
(2022): Optimal hormone replacement therapy in hypothyroidism - a model predictive control approach, Frontiers in Endocrinology, vol. 13, no. 884018
DOI: 10.3389/fendo.2022.884018 -
(2022): Data-driven distributed MPC of dynamically coupled linear systems, IFAC-PapersOnLine, Vol. 55, Issue 30, pp. 365-370, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2022)
DOI: 10.1016/j.ifacol.2022.11.080 -
(2022): Sample-based observability of linear discrete-time systems, 2022 IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, pp. 4199-4205
DOI: 10.1109/CDC51059.2022.9993267 -
(2022): Mathematical Modeling and Simulation of Thyroid Homeostasis: Implications for the Allan-Herndon-Dudley Syndrome, Frontiers in Endocrinology, Vol. 13, No. 882788
DOI: 10.3389/fendo.2022.882788 -
(2022): Practical exponential stability of a robust data-driven nonlinear predictive control scheme, Supplementary material - technical report
arXiv: 2204.01150 -
(2022): On a continuous-time version of Willems' lemma, IEEE 61st Conference on Decision and Control (CDC), pp. 2759-2764
DOI: 10.1109/CDC51059.2022.9992347 -
(2021): Data-Based System Analysis and Control of Flat Nonlinear Systems, 60th IEEE Conference on Decision and Control (CDC), pp. 1484-1489
DOI: 10.1109/CDC45484.2021.9683327
arXiv: 2103.02892 -
(2021): Data-driven model predictive control: closed-loop guarantees and experimental results, at–Automatisierungstechnik, vol. 69, no. 7, pp. 608-618
DOI: 10.1515/auto-2021-0024
