Robust and Efficient Hybrid Optimal Control via Gaussian Process Regression and Multiple Shooting With Experimental Validation on a Double Pendulum on a Cart
M. Hesse, L. Schwarzer, J. Timmermann, A. Trächtler, PAMM 25 (2025).

DART - Datengetriebene Methoden in der Regelungstechnik
J. Timmermann, R.-S. Götte, A. Junker, M. Hesse, L. Schwarzer, DART - Datengetriebene Methoden in der Regelungstechnik, 1. Auflage, HNI Verlagsschriftenreihe, Paderborn, 2025.

Adaptive DataDriven Models in PortHamiltonian Form for Control Design
A. Junker, J. Timmermann, A. Trächtler, PAMM 25 (2024).

Online Learning With Joint State and Model Estimation
R.-S. Götte, J. Timmermann, PAMM 25 (2024).

Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
R.-S. Götte, J. Timmermann, in: 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), 2023, pp. 85–90.

Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF
R.-S. Götte, J. Timmermann, in: IFAC-PapersOnLine, 2023, pp. 869–874.

Data-driven identification of disturbances using a sliding mode observer
R.-S. Götte, J.N. Klusmann, J. Timmermann, in: Proceedings - 33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023, 2023, pp. 113–123.

Hybrid Optimal Control for Dynamical Systems using Gaussian Process Regression and Unscented Transform<sup>*</sup>
M. Hesse, J. Timmermann, A. Trächtler, in: 2023 European Control Conference (ECC), IEEE, 2023.

Adaptive Koopman-Based Models for Holistic Controller and Observer Design
A. Junker, K.E.F. Pape, J. Timmermann, A. Trächtler, IFAC-PapersOnLine 56 (2023) 625–630.

Autonomous Golf Putting with Data-Driven and Physics-Based Methods
A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: 2022 Sixth IEEE International Conference on Robotic Computing (IRC), IEEE, 2023.

Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering
R.-S. Götte, J. Timmermann, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2022, pp. 67–76.

Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
O. Schön, R.-S. Götte, J. Timmermann, in: 14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022), 2022, pp. 19–24.

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

Data-Driven Models for Control Engineering Applications Using the Koopman Operator
A. Junker, J. Timmermann, A. Trächtler, in: 2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022), 2022, pp. 1–9.

Autonomes Putten mittels datengetriebener und physikbasierter Methoden
A. Junker, N. Fittkau, J. Timmermann, A. Trächtler, in: Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022, 2022, pp. 119–124.

Learning Data-Driven PCHD Models for Control Engineering Applications*
A. Junker, J. Timmermann, A. Trächtler, IFAC-PapersOnLine 55 (2022) 389–394.

Intelligente Steuerungen und Regelungen
C. Lüke, J. Timmermann, J.H. Kessler, A. Trächtler, in: Steigerung Der Intelligenz Mechatronischer Systeme, Springer Vieweg, 2018, pp. 153–192.

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

Nonlinear Model Predictive Control with Discrete Mechanics and Optimal Control
K. Xu, J. Timmermann, A. Trächtler, in: Proc. Advanced Intelligent Mechatronics (AIM), IEEE, 2017.

Swing-up of the moving double pendulum on a cart with simulation based LQR-Trees
K. Xu, J. Timmermann, A. Trächtler, in: Proc. 20th IFAC World Congress, 2017.