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Institut für Systemtheorie und Regelungstechnik
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Schlüter

Henning Schlüter

Herr M.Sc.

Wissenschaftlicher Mitarbeiter
Institut für Systemtheorie und Regelungstechnik

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+49 711 685 66312
+49 711 685 67735
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Pfaffenwaldring 9
70569 Stuttgart
Deutschland
Raum: 3.234

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Fachgebiet

Ich arbeite auf dem Gebiet der Modellprädiktive Regelung (MPC), insbesondere unter Berücksichtigung von Systemen, die erheblichen stochastischen Unsicherheiten unterliegen. Gegenwärtig liegt der Schwerpunkt auf dem Entwurf neuer Regelungsalgorithmen, die probabilistische Beschränkungen für das System durchsetzen, obwohl es möglicherweise großen Störungen und parametrischen Unsicherheiten ausgesetzt ist, die beispielsweise durch eine bekannte Gauß-Verteilung modelliert werden. Zu diesem Zweck werden die stochastischen Auswirkungen auf die vorhergesagte zukünftige Entwicklung des Systems offline berücksichtigt, um ein einfacheres deterministisches Optimierungsproblem für einen schnellen Online-Betrieb zu konstruieren. Wir haben ein neuartiges Initialisierungsschema eingeführt, um die Lösbarkeit des online Optimierungsproblems sicherzustellen, die mit den realisierten Verletzungen einhergehen, die eine probabilistische Interpretation der Einschränkungen ermöglicht. Darüber hinaus arbeite ich an nicht-linearen Varianten von stochastischen Regelung MPC, bei denen Systemeigenschaften wie die inkrementelle Stabilisierbarkeit ausgenutzt werden. Insgesamt konzentriert sich meine Forschung auf die Ableitung strenger Garantien für die entwickelten MPC Algorithmen.

Google-scholar profile	0000-0001-6057-2404
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Publikationen

H. Schlüter and F. Allgöwer, “Stochastic Model Predictive Control using Initial State and Variance Interpolation,” in Proc. 62nd IEEE Conference on Decision and Control (CDC), Singapore: IEEE, Dec. 2023. doi: 10.1109/CDC49753.2023.10383711.
Zusammenfassung
We present a Stochastic Model Predictive Control (SMPC) framework for linear systems subject to Gaussian disturbances. In order to avoid feasibility issues, we employ a recent initialization strategy, optimizing over an interpolation of the initial state between the current measurement and previous prediction. By also considering the variance in the interpolation, we can employ variable-size tubes, to ensure constraint satisfaction in closed-loop. We show that this novel method improves control performance and enables following the constraint closer, then previous methods. Using a DC-DC converter as numerical example we illustrated the improvement over previous methods.
BibTeX
@inproceedings{ist:schlueter2023b, abstract = {We present a Stochastic Model Predictive Control (SMPC) framework for linear systems subject to Gaussian disturbances. In order to avoid feasibility issues, we employ a recent initialization strategy, optimizing over an interpolation of the initial state between the current measurement and previous prediction. By also considering the variance in the interpolation, we can employ variable-size tubes, to ensure constraint satisfaction in closed-loop. We show that this novel method improves control performance and enables following the constraint closer, then previous methods. Using a DC-DC converter as numerical example we illustrated the improvement over previous methods.}, address = {Singapore}, archiveprefix = {arXiv}, author = {Schlüter, Henning and Allgöwer, Frank}, booktitle = {Proc. 62nd IEEE Conference on Decision and Control (CDC)}, day = 15, doi = {10.1109/CDC49753.2023.10383711}, eprint = {2304.07122}, month = {12}, preprinturl = {https://arxiv.org/abs/2304.07122}, primaryclass = {eess.SY}, publisher = {IEEE}, title = {Stochastic Model Predictive Control using Initial State and Variance Interpolation}, year = 2023 }
DOI
10.1109/CDC49753.2023.10383711
M. Zimmer et al., “In vivo Assessment of Shear Wave Propagation in Pennate Muscles using an Automatic Ultrasound Probe Alignment System,” IEEE Open Journal of Engineering in Medicine and Biology, pp. 1–9, Nov. 2023, doi: 10.1109/OJEMB.2023.3338090.
Zusammenfassung
Goal: Skeletal muscle mechanics can be assessed in vivo using shear wave elastography. However, the impact of pennation angle on shear wave velocity (SWV) remains unclear. This study aims to quantify the effect by automatically aligning the ultrasound probe with muscle fiber orientation. Methods: We propose an automatic ultrasound probe alignment system and compare it to manual and no alignment. SWV of the gastrocnemius medialis muscle of ten volunteers was measured during rest and isometric contractions. Results: The SWV was different between the conditions ($p~=~0.008$). The highest SWV was obtained during the automatic alignment and differences between the conditions were most pronounced during high-level contractions. The automatic system yielded more accurate alignment compared to a manual operator ($p~=~0.05$). Conclusions: The present study indicates that pennation angle affects SWV, hence muscle fiber orientation must be considered to reliably interpret SWV. Using automatic alignment systems allows for more accurate alignment, improving the methodology of ultrasound elastography in skeletal muscles.
BibTeX
@article{ist:schluter2023c, abstract = {Goal: Skeletal muscle mechanics can be assessed in vivo using shear wave elastography. However, the impact of pennation angle on shear wave velocity (SWV) remains unclear. This study aims to quantify the effect by automatically aligning the ultrasound probe with muscle fiber orientation. Methods: We propose an automatic ultrasound probe alignment system and compare it to manual and no alignment. SWV of the gastrocnemius medialis muscle of ten volunteers was measured during rest and isometric contractions. Results: The SWV was different between the conditions ($p~=~0.008$). The highest SWV was obtained during the automatic alignment and differences between the conditions were most pronounced during high-level contractions. The automatic system yielded more accurate alignment compared to a manual operator ($p~=~0.05$). Conclusions: The present study indicates that pennation angle affects SWV, hence muscle fiber orientation must be considered to reliably interpret SWV. Using automatic alignment systems allows for more accurate alignment, improving the methodology of ultrasound elastography in skeletal muscles.}, author = {Zimmer, Manuela and Bunz, Elsa K. and Ehring, Tobias and Kaiser, Benedikt and Kienzlen, Annika and Schlüter, Henning and Zürn, Manuel}, doi = {10.1109/OJEMB.2023.3338090}, issn = {2644-1276}, journal = {IEEE Open Journal of Engineering in Medicine and Biology}, month = {11}, pages = {1-9}, title = {In vivo Assessment of Shear Wave Propagation in Pennate Muscles using an Automatic Ultrasound Probe Alignment System}, year = 2023 }
DOI
10.1109/OJEMB.2023.3338090
Z. Ma, H. Schlüter, F. Berkel, T. Specker, and F. Allgöwer, “Recursive Feasibility and Stability for Stochastic MPC based on Polynomial Chaos,” in Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS), Canberra, Australia: Elsevier, Jan. 2023, pp. 204–209. doi: 10.1016/j.ifacol.2023.02.035.
Zusammenfassung
This paper deals with stochastic model predictive control (SMPC) based on polynomial chaos expansion (PCE) for linear systems with time-invariant stochastic parametric uncertainties and time-varying stochastic additive disturbances subject to chance constraints on states and inputs. Exploiting terminal ingredients in the SMPC problem and a hybrid update strategy, a recursively feasible optimization problem is formulated. Moreover, stability of the system of PCE coefficients can be shown. Furthermore, in the paper the performance and computational complexity of SMPC based on PCEs is compared to tube-based SMPC and robust model predictive control (RMPC) is analyzed and benefits are demonstrated in simulation.
BibTeX
@inproceedings{ist:schlueter2023a, abstract = {This paper deals with stochastic model predictive control (SMPC) based on polynomial chaos expansion (PCE) for linear systems with time-invariant stochastic parametric uncertainties and time-varying stochastic additive disturbances subject to chance constraints on states and inputs. Exploiting terminal ingredients in the SMPC problem and a hybrid update strategy, a recursively feasible optimization problem is formulated. Moreover, stability of the system of PCE coefficients can be shown. Furthermore, in the paper the performance and computational complexity of SMPC based on PCEs is compared to tube-based SMPC and robust model predictive control (RMPC) is analyzed and benefits are demonstrated in simulation.}, address = {Canberra, Australia}, author = {Ma, Zhiming and Schlüter, Henning and Berkel, Felix and Specker, Thomas and Allgöwer, Frank}, booktitle = {Proc. 12th IFAC Symp. Nonlinear Control Systems (NOLCOS)}, doi = {10.1016/j.ifacol.2023.02.035}, eventdate = {2023-01-05}, eventtitle = {12th IFAC Symposium on Nonlinear Control Systems}, issn = {2405-8963}, month = {01}, number = 1, pages = {204-209}, publisher = {Elsevier}, title = {Recursive Feasibility and Stability for Stochastic MPC based on Polynomial Chaos}, volume = 56, year = 2023 }
DOI
10.1016/j.ifacol.2023.02.035
H. Schlüter and F. Allgöwer, “Stochastic model predictive control using initial state optimization,” in Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems (MTNS), Bayreuth, Germany: Elsevier, Nov. 2022, pp. 454–459. doi: 10.1016/j.ifacol.2022.11.095.
Zusammenfassung
We propose a stochastic MPC scheme using an optimization over the initial state for the predicted trajectory. Considering linear discrete-time systems under unbounded additive stochastic disturbances subject to chance constraints, we use constraint tightening based on probabilistic reachable sets to design the MPC. The scheme avoids the infeasibility issues arising from unbounded disturbances by including the initial state as a decision variable. We show that the stabilizing control scheme can guarantee constraint satisfaction in closed loop, assuming unimodal disturbances. In addition to illustrating these guarantees, the numerical example indicates further advantages of optimizing over the initial state for the transient behavior.
BibTeX
@inproceedings{ist:schluter2022a, abstract = {We propose a stochastic MPC scheme using an optimization over the initial state for the predicted trajectory. Considering linear discrete-time systems under unbounded additive stochastic disturbances subject to chance constraints, we use constraint tightening based on probabilistic reachable sets to design the MPC. The scheme avoids the infeasibility issues arising from unbounded disturbances by including the initial state as a decision variable. We show that the stabilizing control scheme can guarantee constraint satisfaction in closed loop, assuming unimodal disturbances. In addition to illustrating these guarantees, the numerical example indicates further advantages of optimizing over the initial state for the transient behavior.}, address = {Bayreuth, Germany}, author = {Schl{\"u}ter, Henning and Allg{\"o}wer, Frank}, booktitle = {Proc. 25th Int. Symp. Mathematical Theory of Networks and Systems (MTNS)}, doi = {10.1016/j.ifacol.2022.11.095}, eventdate = {2022-09-12}, eventtitle = {Int. Symp. Mathematical Theory of Networks and Systems (MTNS)}, month = {11}, number = 30, pages = {454-459}, preprinturl = {https://arxiv.org/abs/2203.01844}, publisher = {Elsevier}, title = {Stochastic model predictive control using initial state optimization}, url = {https://arxiv.org/abs/2203.01844}, volume = 55, year = 2022 }
Link
https://arxiv.org/abs/2203.01844
DOI
10.1016/j.ifacol.2022.11.095
H. Schlüter, F. Solowjow, and S. Trimpe, “Event-triggered Learning for Linear Quadratic Control,” IEEE Transactions on Automatic Control, vol. 66, Art. no. 10, Oct. 2020, doi: 10.1109/tac.2020.3030877.
Zusammenfassung
When models are inaccurate, the performance of model-based control will degrade. For linear quadratic control, an event-triggered learning framework is proposed that automatically detects inaccurate models and triggers the learning of a new process model when needed. This is achieved by analyzing the probability distribution of the linear quadratic cost and designing a learning trigger that leverages Chernoff bounds. In particular, whenever empirically observed cost signals are located outside the derived confidence intervals, we can provably guarantee that this is with high probability due to a model mismatch. With the aid of numerical and hardware experiments, we demonstrate that the proposed bounds are tight and that the event-triggered learning algorithm effectively distinguishes between inaccurate models and probabilistic effects, such as process noise. Thus, a structured approach is obtained that decides when model learning is beneficial.
BibTeX
@article{schluter2020eventtriggered, abstract = {When models are inaccurate, the performance of model-based control will degrade. For linear quadratic control, an event-triggered learning framework is proposed that automatically detects inaccurate models and triggers the learning of a new process model when needed. This is achieved by analyzing the probability distribution of the linear quadratic cost and designing a learning trigger that leverages Chernoff bounds. In particular, whenever empirically observed cost signals are located outside the derived confidence intervals, we can provably guarantee that this is with high probability due to a model mismatch. With the aid of numerical and hardware experiments, we demonstrate that the proposed bounds are tight and that the event-triggered learning algorithm effectively distinguishes between inaccurate models and probabilistic effects, such as process noise. Thus, a structured approach is obtained that decides when model learning is beneficial.}, author = {Schl\"uter, Henning and Solowjow, Friedrich and Trimpe, Sebastian}, day = 13, doi = {10.1109/tac.2020.3030877}, journal = {{IEEE} Transactions on Automatic Control}, month = {10}, number = 10, pages = {4485 - 4498}, preprinturl = {https://arxiv.org/abs/1910.07732}, publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, title = {Event-triggered Learning for Linear Quadratic Control}, url = {https://doi.org/10.48550/arxiv.1910.07732}, volume = 66, year = 2020 }
Link
https://doi.org/10.48550/arxiv.1910.07732
DOI
10.1109/tac.2020.3030877
H. Schlüter and F. Allgöwer, “A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances,” in Proc. 21th IFAC World Congress, Berlin, Germany, Jul. 2020, pp. 7130–7135. doi: 10.1016/j.ifacol.2020.12.518.
Zusammenfassung
This paper presents a nonlinear model predictive control strategy for stochastic systems with state- and input-dependent, finite-support disturbances subject to individual chance constraints. Our approach uses an online computed stochastic tube to ensure stability, constraint satisfaction, and recursive feasibility in the presence of stochastic uncertainties. The shape of the tube and the constraint backoff is based on an offline computed incremental Lyapunov function.
BibTeX
@inproceedings{ist:schlueter20a, abstract = {This paper presents a nonlinear model predictive control strategy for stochastic systems with state- and input-dependent, finite-support disturbances subject to individual chance constraints. Our approach uses an online computed stochastic tube to ensure stability, constraint satisfaction, and recursive feasibility in the presence of stochastic uncertainties. The shape of the tube and the constraint backoff is based on an offline computed incremental Lyapunov function.}, address = {Berlin, Germany}, author = {Schl{\"u}ter, H. and Allg{\"o}wer, F.}, booktitle = {{P}roc. 21th {IFAC} {W}orld {C}ongress}, doi = {10.1016/j.ifacol.2020.12.518}, month = {07}, number = 2, pages = {7130-7135}, preprinturl = {https://arxiv.org/abs/1912.01946}, title = {A Constraint-Tightening Approach to Nonlinear Stochastic Model Predictive Control under General Bounded Disturbances}, url = {https://arxiv.org/abs/1912.01946}, volume = 53, year = 2020 }
Link
https://arxiv.org/abs/1912.01946
DOI
10.1016/j.ifacol.2020.12.518
H. Schlüter, P. Schillinger, and M. Bürger, “On the Design of Penalty Structures for Minimum-Violation LTL Motion Planning,” in 2018 IEEE Conference on Decision and Control (CDC), Dec. 2018, pp. 4153–4158. doi: 10.1109/CDC.2018.8619148.
Zusammenfassung
This paper studies the problem of penalizing rule violation in the context of logic-based motion planning. Translating a given Linear Temporal Logic (LTL) rule into a penalty structure requires a design decision, since the discrete automata obtained from the rule do not provide a straightforward method to penalize rule violation. We propose a design method that explicitly specifies violation to allow for more flexibility in parametrization of desired behaviors and differentiation of penalty semantics. Case study results are shown in the context of an autonomous driving scenario.
BibTeX
@inproceedings{schluter2018design, abstract = {This paper studies the problem of penalizing rule violation in the context of logic-based motion planning. Translating a given Linear Temporal Logic (LTL) rule into a penalty structure requires a design decision, since the discrete automata obtained from the rule do not provide a straightforward method to penalize rule violation. We propose a design method that explicitly specifies violation to allow for more flexibility in parametrization of desired behaviors and differentiation of penalty semantics. Case study results are shown in the context of an autonomous driving scenario.}, author = {{Schlüter}, H. and {Schillinger}, P. and {Bürger}, M.}, booktitle = {2018 {IEEE} Conference on Decision and Control ({CDC})}, doi = {10.1109/CDC.2018.8619148}, month = {12}, pages = {4153-4158}, title = {On the Design of Penalty Structures for Minimum-Violation {LTL} Motion Planning}, year = 2018 }
DOI
10.1109/CDC.2018.8619148

Lehre

SS 2024	Projektwettbewerb: Einführung in die Regelungstechnik Projektwettbewerb: Konzepte der Regelungstechnik
WS 2023	Einführung in die Regelungstechnik (Wochenprogramm und Gruppenübungen)
SS 2023	Projektwettbewerb: Einführung in die Regelungstechnik Projektwettbewerb: Konzepte der Regelungstechnik
SS 2022	Proseminar Technische Kybernetik Model Predictive Control
WS 2021	Einführung in die Regelungstechnik (Wochenprogramm und Gruppenübungen)
SS 2021	Proseminar Technische Kybernetik
WS 2020	Einführung in die Regelungstechnik (Wochenprogramm und Gruppenübungen)
SS 2020	Proseminar Technische Kybernetik Systemdynamische Grundlagen der Regelungstechnik (Gruppenübungen)
WS 2019	Einführung in die Regelungstechnik (Gruppenübungen) Praktikum Konzepte der Regelungstechnik
SS 2019	Proseminar Technische Kybernetik

Lebenslauf

seit 05/2019	Wissenschaftliche Mitarbeiter am Institut für Systemtheorie und Regelungstechnik, Universität Stuttgart
10/2016 − 04/2019	Studium der "Technischen Kybernetik" (M.Sc.) an der Universität Stuttgart Masterarbeit: "Event-Trigered Learning for Control" bei Dr. Sebastian Trimpe mit der Intelligent Control Systems Group des Max Planck Institute for Intelligent Systems.
10/2017 − 03/2018	Praktikum beim Bosch Center for Artificial Intelligence (BCAI) der Robert Bosch GmbH, Renningen Thema: Minimum-Violation LTL Motion Planning (Publikation: [DOI])
10/2013 − 11/2016	Studium der "Technischen Kybernetik" (B.Sc.) an der Universität Stuttgart Bachelorarbeit: "3D-Mapping using Planar Surface SLAM on Mobile Robots" bei Prof. Marc Toussaint am Machine Learning & Robotics Lab der Universität Stuttgart.

Henning Schlüter

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