Götte, R.-S., &; Timmermann, J. (2023). Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF. 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 56(1), 85–90.https://doi.org/10.1016/j.ifacol.2023.02.015 
LibreCat: /record/34171
DOI: https://doi.org/10.1016/j.ifacol.2023.02.015

Junker, A., Fittkau, N., Timmermann, J., &; Trächtler, A. (2023). Autonomous Golf Putting with Data-Driven and Physics-Based Methods. 2022 Sixth IEEE International Conference on Robotic Computing (IRC). 2022 Sixth IEEE International Conference on Robotic Computing (IRC), Naples, Italy.https://doi.org/10.1109/irc55401.2022.00031 
LibreCat: /record/42238
DOI: 10.1109/irc55401.2022.00031

Hesse, M., Timmermann, J., &; Trächtler, A. (2023). Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>. 2023 European Control Conference (ECC).https://doi.org/10.23919/ecc57647.2023.10178368 
LibreCat: /record/48476
DOI: 10.23919/ecc57647.2023.10178368

Götte, R.-S., Klusmann, J. N., &; Timmermann, J. (2023). Data-driven identification of disturbances using a sliding mode observer. Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023, 113–123.https://doi.org/10.5445/KSP/1000162754 
LibreCat: /record/48482
DOI: 10.5445/KSP/1000162754

Götte, R.-S., &; Timmermann, J. (2023). Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF. IFAC-PapersOnLine, 56(2), 869–874.
LibreCat: /record/44326

Junker, A., Pape, K. E. F., Timmermann, J., &; Trächtler, A. (2023). Adaptive Koopman-Based Models for Holistic Controller and Observer Design. IFAC-PapersOnLine, 56(3), 625–630.https://doi.org/10.1016/j.ifacol.2023.12.094 
LibreCat: /record/50070
DOI: 10.1016/j.ifacol.2023.12.094

Junker, A., Timmermann, J., &; Trächtler, A. (n.d.). Data-Driven Models for Control Engineering Applications Using the Koopman Operator. 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), 1–9.https://doi.org/10.1109/AIRC56195.2022.9836980 
LibreCat: /record/26389
DOI: 10.1109/AIRC56195.2022.9836980

Götte, R.-S., &; Timmermann, J. (2022). Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering. 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 67–76.https://doi.org/10.1109/AIRC56195.2022.9836982 
LibreCat: /record/26539
DOI: 10.1109/AIRC56195.2022.9836982

Schön, O., Götte, R.-S., &; Timmermann, J. (2022). Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems. 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 55(12), 19–24.https://doi.org/10.1016/j.ifacol.2022.07.282 
LibreCat: /record/31066
DOI: https://doi.org/10.1016/j.ifacol.2022.07.282

Hesse, M., Hunstig, M., Timmermann, J., &; Trächtler, A. (2022). Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design. Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 383–394.
LibreCat: /record/29803

Junker, A., Fittkau, N., Timmermann, J., &; Trächtler, A. (2022). Autonomes Putten mittels datengetriebener und physikbasierter Methoden. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022, 119–124.https://doi.org/10.5445/KSP/1000151141 
LibreCat: /record/34011
DOI: 10.5445/KSP/1000151141

Junker, A., Timmermann, J., &; Trächtler, A. (2022). Learning Data-Driven PCHD Models for Control Engineering Applications*. IFAC-PapersOnLine, 55(12), 389–394.https://doi.org/10.1016/j.ifacol.2022.07.343 
LibreCat: /record/50071
DOI: 10.1016/j.ifacol.2022.07.343

Lüke, C., Timmermann, J., Kessler, J. H., &; Trächtler, A. (2018). Intelligente Steuerungen und Regelungen. In Steigerung der Intelligenz mechatronischer Systeme (Vol. 1, pp. 153–192). Springer Vieweg.
LibreCat: /record/22984

Hesse, M., Timmermann, J., Hüllermeier, E., &; Trächtler, A. (2018). A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. Procedia Manufacturing, 24, 15–20.
LibreCat: /record/22996

Xu, K., Timmermann, J., &; Trächtler, A. (2017). Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control. In Proc. Advanced Intelligent Mechatronics (AIM). IEEE.
LibreCat: /record/23005

Xu, K., Timmermann, J., &; Trächtler, A. (2017). Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees. In Proc. 20th IFAC World Congress.
LibreCat: /record/23006

Flaßkamp, K., Timmermann, J., Ober-Blöbaum, S., &; Trächtler, A. (2014). Control strategies on stable manifolds for energyefficient swing-ups of double pendula. International Journal of Control, DOI: 10.1080/00207179.2014.893450, 1–20.
LibreCat: /record/23110

Timmermann, J. (2014). Optimale Steuerung und Mehrzieloptimierung von dynamischen Systemen untersucht am Beispiel des Mehrfachpendels.
LibreCat: /record/42237

Flaßkamp, K., Timmermann, J., Ober-Blöbaum, S., Dellnitz, M., &; Trächtler, A. (2012). Optimal Control on Stable Manifolds for a Double Pendulum. Proceedings in Applied Mathematics and Mechanics, 12(1), 723–724.
LibreCat: /record/23161

Bielawny, D., Krüger, M., Reinold, P., Timmermann, J., &; Trächtler, A. (2011). Iterative learning of Stochastic Disturbance Profiles Using Bayesian Networks. In 9th International Conference on Industrial Informatics (INDIN).
LibreCat: /record/23182