Publications of PN 4

R. Strässer, M. Schaller, K. Worthmann, J. Berberich, and F. Allgöwer, “Koopman-based feedback design with stability guarantees,” IEEE Transactions on Automatic Control, vol. 70, Art. no. 1, 2025, doi: 10.1109/TAC.2024.3425770.

R. Strässer, S. Schlor, and F. Allgöwer, “Decrypting Nonlinearity: Koopman Interpretation and Analysis of Cryptosystems,” Automatica, vol. 173, p. 112022, 2025, doi: 10.1016/j.automatica.2024.112022.

M. Rosenfelder, H. Ebel, and P. Eberhard, “Force-Based Organization and Control Scheme for the Non-Prehensile Cooperative Transportation of Objects,” Robotica, vol. 42, Art. no. 2, 2024, doi: 10.1017/S0263574723001704.

M. Seidel, M. Hertneck, P. Yu, S. Linsenmayer, D. V. Dimarogonas, and F. Allgöwer, “A Window-based Periodic Event-triggered Consensus Scheme for Multi-agent Systems,” IEEE Transactions on Control of Network Systems, vol. 11, Art. no. 1, Mar. 2024, doi: 10.1109/tcns.2023.3285863.

T. Dai et al., “Modeling and Experimental Validation of High‐Flow Fluid‐Driven Membrane Valves for Hyperactuated Soft Robots,” Advanced Intelligent Systems, May 2024, doi: 10.1002/aisy.202300864.

J. Chen, W. Luo, H. Ebel, and P. Eberhard, “Optimization-Based Trajectory Planning for Transport Collaboration of Heterogeneous Systems,” at - Automatisierungstechnik, vol. 72, Art. no. 2, 2024, doi: 10.1515/auto-2023-0078.

H. Ebel, M. Rosenfelder, and P. Eberhard, “Cooperative Object Transportation with Differential-Drive Mobile Robots: Control and Experimentation,” Robotics and Autonomous Systems, vol. 173, p. 104612, 2024, doi: 10.1016/j.robot.2023.104612.

T. J. Meijer, T. Holicki, S. J. A. M. v. d. Eijnden, C. W. Scherer, and W. P. M. H. Heemels, “The Non-Strict Projection Lemma,” IEEE Transactions on Automatic Control, pp. 1–8, 2024, doi: 10.1109/TAC.2024.3371374.

B. Song and A. Iannelli, “The role of identification in data-driven policy iteration: A system theoretic study,” International Journal of Robust and Nonlinear Control, vol. n/a, Art. no. n/a, 2024, doi: https://doi.org/10.1002/rnc.7475.

T. Holicki, J. Nicodemus, P. Schwerdtner, and B. Unger, “Energy matching in reduced passive and port-Hamiltonian systems.” 2023. doi: 10.48550/arXiv.2309.05778.

F. A. Taha, S. Yan, and E. Bitar, “A Distributionally Robust Approach to Regret Optimal Control using the Wasserstein Distance,” in 2023 62nd IEEE Conference on Decision and Control (CDC), 2023, pp. 2768–2775. doi: 10.1109/CDC49753.2023.10384311.

S. Schlor, R. Strässer, and F. Allgöwer, “Koopman interpretation and analysis of a public-key cryptosystem: Diffie-Hellman key exchange,” in Proceedings of the 22nd IFAC World Congress, Yokohama, Japan, 2023, pp. 984–990. doi: 10.1016/j.ifacol.2023.10.1693.

L. Hewing et al., “Enhancing the Guidance, Navigation and Control of Autonomous Parafoils using Machine Learning Methods,” in Papers of ESA GNC-ICATT 2023, in ESA GNC-ICATT 2023. ESA, Jul. 2023. doi: 10.5270/esa-gnc-icatt-2023-135.

R. Strässer, J. Berberich, and F. Allgöwer, “Control of bilinear systems using gain-scheduling: Stability and performance guarantees,” in 62nd IEEE Conference on Decision and Control (CDC), Singapore, Singapore, 2023, pp. 4674–4681. doi: 10.1109/CDC49753.2023.10384021.

A. Baier, D. Aspandi, and S. Staab, “ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, International Joint Conferences on Artificial Intelligence Organization, Aug. 2023.

R. Strässer, J. Berberich, and F. Allgöwer, “Robust data-driven control for nonlinear systems using the Koopman operator,” in Proceedings of the 22nd IFAC World Congress, 2023, pp. 2257–2262. doi: https://doi.org/10.1016/j.ifacol.2023.10.1190.

M. Rosenfelder, H. Ebel, and P. Eberhard, “A Force-Based Formation Synthesis Approach for the Cooperative Transportation of Objects,” in Advances in Service and Industrial Robotics, T. Petrič, A. Ude, and L. Žlajpah, Eds., in Mechanisms and Machine Science, vol. 135. Springer, 2023, pp. 317–324. doi: 10.1007/978-3-031-32606-6_37.

O. V. Martynenko et al., “Development and verification of a physiologically motivated internal controller for the open-source extended Hill-type muscle model in LS-DYNA,” Biomechanics and Modeling in Mechanobiology, vol. 22, Art. no. 6, 2023, doi: 10.1007/s10237-023-01748-9.

T. Holicki and C. W. Scherer, “IQC based analysis and estimator design for discrete-time systems affected by impulsive uncertainties,” Nonlinear Analysis: Hybrid Systems, vol. 50, p. 101399, Nov. 2023, doi: 10.1016/j.nahs.2023.101399.

T. Holicki and C. W. Scherer, “Input-Output-Data-Enhanced Robust Analysis via Lifting,” IFAC-PapersOnLine, vol. 56, Art. no. 2, 2023, doi: 10.1016/j.ifacol.2023.10.047.

C. A. Rösinger and C. W. Scherer, “Gain-Scheduling Controller Synthesis for Nested Systems With Full Block Scalings,” IEEE Transactions on Automatic Control, pp. 1–16, 2023, doi: 10.1109/TAC.2023.3329851.

M. M. Morato, T. Holicki, and C. W. Scherer, “Stabilizing Model Predictive Control Synthesis using Integral Quadratic Constraints and Full-Block Multipliers,” International Journal of Robust and Nonlinear Control, vol. 33, Art. no. 18, 2023, doi: https://doi.org/10.1002/rnc.6952.

T. Martin and F. Allgöwer, “Data-driven inference on optimal input-output properties of polynomial systems with focus on nonlinearity measures,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 5, 2023, doi: 10.1109/TAC.2022.3226652.

T. Martin and F. Allgöwer, “Data-driven system analysis of nonlinear systems using polynomial approximation,” IEEE Trans. Automat. Control (early access), 2023, doi: 10.1109/TAC.2023.3321212.

T. Martin, T. B. Schön, and F. Allgöwer, “Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey,” Annual Reviews in Control, vol. 56, p. 100911, 2023, doi: 10.1016/j.arcontrol.2023.100911.

D. Gramlich, T. Holicki, C. W. Scherer, and C. Ebenbauer, “A Structure Exploiting SDP Solver for Robust Controller Synthesis,” IEEE Control Syst. Lett., vol. 7, pp. 1831–1836, 2023, doi: 10.1109/LCSYS.2023.3277314.

A. Kharitenko and C. Scherer, “Time-varying Zames–Falb multipliers for LTI Systems are superfluous,” Automatica, vol. 147, p. 110577, Jan. 2023, doi: 10.1016/j.automatica.2022.110577.

C. D. Remy, Z. Brei, D. Bruder, J. Remy, K. Buffinton, and R. B. Gillespie, “The “Fluid Jacobian”: Modeling force-motion relationships in fluid-driven soft robots,” The International Journal of Robotics Research, Nov. 2023, doi: 10.1177/02783649231210592.

T. Monninger et al., “SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks,” IEEE Robotics and Automation Letters, pp. 1–8, 2023, doi: 10.1109/LRA.2023.3234771.

A. Alanwar, A. Koch, F. Allgöwer, and F. H. Johansson, “Data-Driven Reachability Analysis from Noisy Data,” IEEE Transactions on Automatic Control, pp. 1–16, 2023, doi: 10.1109/TAC.2023.3257167.

C. W. Scherer, “Robust Exponential Stability and Invariance Guarantees with General Dynamic O’Shea-Zames-Falb Multipliers,” IFAC-PapersOnLine, vol. 56, Art. no. 2, 2023, doi: 10.1016/j.ifacol.2023.10.556.

J. Berberich, C. W. Scherer, and F. Allgower, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 8, 2023, doi: 10.1109/tac.2022.3209342.

V. Wagner, R. Strässer, F. Allgöwer, and N. E. Radde, “A provably convergent control closure scheme for the Method of Moments of the Chemical Master Equation,” Journal of Chemical Theory and Computation, vol. 19, Art. no. 24, Dec. 2023, doi: https://doi.org/10.1021/acs.jctc.3c00548.

T. Holicki, “A Complete Analysis and Design Framework for Linear Impulsive and Related Hybrid Systems,” University of Stuttgart, 2022. doi: 10.18419/opus-12158.

D. Gramlich, C. W. Scherer, and C. Ebenbauer, “Robust Differential Dynamic Programming,” in 2022 IEEE 61st Conference on Decision and Control (CDC), 2022. doi: 10.1109/cdc51059.2022.9992569.

C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning Functions and Uncertainty Sets Using Geometrically Constrained Kernel Regression,” in 61st IEEE Conf. Decision and Control, IEEE, Dec. 2022. doi: 10.1109/cdc51059.2022.9993144.

T. Martin and F. Allgöwer, “Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation,” in Proc. American Control Conf. (ACC), Atlanta, GA, USA, 2022, pp. 1432–1437.

J. Nicodemus, J. Kneifl, J. Fehr, and B. Unger, “Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,” IFAC-PapersOnLine, vol. 55, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.117.

D. Gramlich, C. Ebenbauer, and C. W. Scherer, “Synthesis of Accelerated Gradient Algorithms for Optimization and Saddle Point Problems using Lyapunov functions,” Systems & Control Letters, vol. 165, 2022, doi: 10.1016/j.sysconle.2022.105271.

J. Berberich, C. W. Scherer, and F. Allgöwer, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Transactions on Automatic Control, vol. 68, Art. no. 8, 2022, doi: 10.1109/tac.2022.3209342.

C. Scherer, “Dissipativity and Integral Quadratic Constraints, Tailored computational robustness tests for complex interconnections,” IEEE Control Systems Magazine, vol. 42, Art. no. 3, 2022, [Online]. Available: https://arxiv.org/abs/2105.07401

H. Eschmann, H. Ebel, and P. Eberhard, “Exploration-Exploitation-Based Trajectory Tracking of Mobile Robots Using Gaussian Processes and Model Predictive Control,” Robotica, vol. 41, Art. no. 10, 2022, doi: 10.1017/S0263574723000863.

F. Kempter, L. Lantella, N. Stutzig, J. Fehr, and T. Siebert, “Role of Rotated Head Postures on Volunteer Kinematics and Muscle Activity in Braking Scenarios Performed on a Driving Simulator,” Annals of Biomedical Engineering, vol. 51, Art. no. 4, 2022, doi: 10.1007/s10439-022-03087-9.

C. Fiedler, C. W. Scherer, and S. Trimpe, “Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 7439–7447. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/16912

J. Veenman, C. W. Scherer, C. Ardura, S. Bennani, V. Preda, and B. Girouart, “IQClab: A new IQC based toolbox for robustness analysis and control design,” in IFAC-PapersOnline, 2021, pp. 69–74. doi: 10.1016/j.ifacol.2021.08.583.

C. Fiedler, C. W. Scherer, and S. Trimpe, “Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees,” in 60th IEEE Conference Decision and Control, 2021.

R. Strässer, J. Berberich, and F. Allgöwer, “Data-Driven Control of Nonlinear Systems: Beyond Polynomial Dynamics,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 4344–4351. doi: 10.1109/CDC45484.2021.9683211.

T. Holicki and C. W. Scherer, “Algorithm Design and Extremum Control: Convex Synthesis due to Plant Multiplier Commutation,” in Proc. 60th IEEE Conf. Decision and Control, 2021, pp. 3249–3256. doi: 10.1109/CDC45484.2021.9683012.

A. Alanwar, A. Koch, F. Allgöwer, and K. H. Johansson, “Data-Driven Reachability Analysis Using Matrix Zonotopes,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research, vol. 144. 2021, pp. 163–175.

N. Wieler, J. Berberich, A. Koch, and F. Allgöwer, “Data-Driven Controller Design via Finite-Horizon Dissipativity,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control, in Proceedings of Machine Learning Research, vol. 144. PMLR, 2021, pp. 287–298.

S. Schlor, M. Hertneck, S. Wildhagen, and F. Allgöwer, “Multi-party computation enables secure polynomial control based solely on secret-sharing,” in Proc. 60th IEEE Conf. Decision and Control (CDC), Austin, TX, USA, 2021, pp. 4882–4887. doi: 10.1109/CDC45484.2021.9683026.

S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach,” International Journal of Control, vol. 94, Art. no. 11, 2021, doi: 10.1080/00207179.2020.1745286.

T. Holicki, C. W. Scherer, and S. Trimpe, “Controller Design via Experimental Exploration with Robustness Guarantees,” IEEE Control Systems Letters, vol. 5, Art. no. 2, 2021, doi: 10.1109/LCSYS.2020.3004506.

T. Holicki and C. W. Scherer, “Robust Gain-Scheduled Estimation with Dynamic D-Scalings,” EEE Transactions on Automatic Control, vol. 66, Art. no. 11, 2021, doi: 10.1109/TAC.2021.3052751.

A. Koch, J. M. Montenbruck, and F. Allgöwer, “Sampling Strategies for Data-Driven Inference of Input-Output System Properties,” IEEE Transactions On Automatic Control, vol. 66, pp. 1144–1159, 2021, doi: 10.1109/TAC.2020.2994894.

A. Koch, J. Berberich, and F. Allgöwer, “Provably robust verification of dissipativity properties from data,” IEEE Transactions on Automatic Control, vol. 67, Art. no. 8, 2021, doi: 10.1109/TAC.2021.3116179.

C. Scherer and C. Ebenbauer, “Convex Synthesis of Accelerated Gradient Algorithms,” SIAM Journal on Control and Optimization, vol. 59, Art. no. 6, 2021, doi: 10.1137/21M1398598.

A. Koch, J. Berberich, J. Köhler, and F. Allgöwer, “Determining optimal input–output properties: A data-driven approach,” Automatica, vol. 134, p. 109906, 2021, doi: https://doi.org/10.1016/j.automatica.2021.109906.

T. Holicki and C. W. Scherer, “Revisiting and Generalizing the Dual Iteration for Static and Robust Output-Feedback Synthesis,” Int. J. Robust Nonlin., vol. 31, Art. no. 11, 2021, doi: 10.1002/rnc.5547.

T. Martin and F. Allgöwer, “Dissipativity verification with guarantees for polynomial systems from noisy input-state data,” IEEE Control Systems Letters, vol. 5, Art. no. 4, 2021, doi: 10.1109/LCSYS.2020.3037842.

T. Holicki and C. W. Scherer, “Output-Feedback Synthesis for a Class of Aperiodic Impulsive Systems,” in IFAC-PapersOnline, 2020, pp. 7299–7304. doi: 10.1016/j.ifacol.2020.12.981.

T. Martin, A. Koch, and F. Allgöwer, “Data-driven surrogate models for LTI systems via saddle-point dynamics,” in Proc. 21st IFAC World Congress, Berlin, Germany, 2020, pp. 971–976. doi: 10.1016/j.ifacol.2020.12.1261.

T. Martin and F. Allgöwer, “Iterative data-driven inference of nonlinearity measures via successive graph approximation,” in Proceedings 59th IEEE Conference Decision and Control (CDC), Jeju, South Korea, 2020, pp. 4760–4765. doi: 10.1109/CDC42340.2020.9304285.

M. Barreau, C. W. Scherer, F. Gouaisbaut, and A. Seuret, “Integral Quadratic Constraints on Linear Infinite-dimensional Systems for Robust Stability Analysis,” in IFAC-PapersOnline, 2020, pp. 7752–7757. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2405896320321297

D. Persson, A. Koch, and F. Allgöwer, “Probabilistic H2-norm estimation via Gaussian process system identification,” in Proceedings 21st IFAC World Congress, Berlin, Germany, 2020, pp. 431–436. doi: 10.1016/j.ifacol.2020.12.211.

A. Koch, J. Berberich, and F. Allgöwer, “Verifying dissipativity properties from noise-corrupted input-state data,” in Proceedings 59th IEEE Conference on Decision and Control (CDC), Jeju, South Korea, 2020, pp. 616–621. doi: 10.1109/CDC42340.2020.9304380.

S. Michalowsky, C. Scherer, and C. Ebenbauer, “Robust and structure exploiting optimisation algorithms: An integral quadratic constraint approach,” International Journal of Control, vol. 2020, pp. 1–24, 2020, doi: 10.1080/00207179.2020.1745286.

D. F. B. Häufle, I. Wochner, D. Holzmüller, D. Drieß, M. Günther, and S. Schmitt, “Muscles reduce neuronal information load : quantification of control effort in biological vs. robotic pointing and walking,” Frontiers in Robotics and AI, vol. 7, p. 77, 2020, doi: 10.3389/frobt.2020.00077.

C. A. Rösinger and C. W. Scherer, “A Flexible Synthesis Framework of Structured Controllers for Networked Systems,” IEEE Trans. Control Netw. Syst., vol. 7, Art. no. 1, 2020, doi: 10.1109/TCNS.2019.2914411.

T. Holicki and C. W. Scherer, “A Homotopy Approach for Robust Output-Feedback Synthesis,” in Proc. 27th. Med. Conf. Control Autom., 2019, pp. 87–93. doi: 10.1109/MED.2019.8798536.

T. Martin and F. Allgöwer, “Nonlinearity Measures for Data-Driven System Analysis and Control,” in Proc. 58th IEEE Conf. Decision and Control (CDC), Nice, France, 2019, pp. 3605–3610. doi: 10.1109/CDC40024.2019.9029804.

C. A. Rösinger and C. W. Scherer, “A Scalings Approach to $H_2$-Gain-Scheduling Synthesis without Elimination,” in IFAC-PapersOnLine, 2019, pp. 50–57. doi: 10.1016/j.ifacol.2019.12.347.

G. Baggio, S. Zampieri, and C. W. Scherer, “Gramian Optimization with Input-Power Constraints,” in 58th IEEE Conf. Decision and Control, 2019, pp. 5686–5691. doi: 10.1109/CDC40024.2019.9029169.

A. Romer, J. Berberich, J. Köhler, and F. Allgöwer, “One-shot verification of dissipativity properties from input-output data,” IEEE Control Systems Letters, vol. 3, pp. 709–714, 2019, doi: 10.1109/LCSYS.2019.2917162.

T. Holicki and C. W. Scherer, “Stability Analysis and Output-Feedback Synthesis of Hybrid Systems Affected by Piecewise Constant Parameters via Dynamic Resetting Scalings,” Nonlinear Analysis: Hybrid Systems, vol. 34, pp. 179–208, 2019, doi: https://doi.org/10.1016/j.nahs.2019.06.003.

A. Romer, J. M. Montenbruck, and F. Allgöwer, “Data-driven inference of conic relations via saddle-point dynamics,” in Proceedings 9th IFAC Symposium Robust Control Design (ROCOND), Florianópolis, Brazil, 2018, pp. 586–591. doi: 10.1016/j.ifacol.2018.11.139.

M. Alvarez Chaves, H. Gupta, U. Ehret, and A. Guthke, “Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data.” 2024. doi: 10.18419/darus-4087.

F. Kempter, L. Lantella, N. Stutzig, J. C. Fehr, and T. Siebert, “Neck Reflex Behavior in Driving Simulator Experiments - Academic-Scale Simulator at ITM.” 2023. doi: 10.18419/darus-3000.

A. Baier and D. Frank, “deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning.” 2023. doi: 10.18419/darus-3455.

A. Baier, D. Aspandi Latif, and S. Staab, “Supplements for “ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks”″.” 2023. doi: 10.18419/darus-3457.

T. Munz, D. Väth, P. Kuznecov, N. T. Vu, and D. Weiskopf, “NMTVis - Neural Machine Translation Visualization System.” 2021. doi: 10.18419/darus-1849.

B. Röder, H. Ebel, and P. Eberhard, “Motion and Motor-Current Data of a Four-Bar Linkage.” 2024. doi: 10.18419/darus-4152.

M. Rosenfelder, H. Ebel, and P. Eberhard, “Experiment Videos of the Force-Based Non-Prehensile Cooperative Transportation of Objects with Mobile Robots.” 2023. doi: 10.18419/darus-3331.

M. Rosenfelder, H. Ebel, and P. Eberhard, “Experiment Videos of a Force-Based Organization and Control Scheme for the Non-Prehensile Cooperative Transportation of Objects.” 2023. doi: 10.18419/darus-3611.

A. Baier and S. Staab, “A Simulated 4-DOF Ship Motion Dataset for System Identification under Environmental Disturbances.” 2022. doi: 10.18419/darus-2905.

L. Nölle et al., “EHTM Code and Manual.” 2022. doi: 10.18419/darus-1144.

H. Eschmann, “A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot.” 2021. doi: 10.18419/darus-1845.