7 citations · 13 across the 8 of their papers we have counts for
8 papers
Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2
In this paper, we propose Lagrangian Gaussian Processes (LGPs) for probabilistic and data-efficient learning of dynamics via discrete forced Euler-Lagrange equations. Importantly,…
Simultaneous State Estimation and Online Model Learning in a Soft Robotic System
Jan-Hendrik Ewering, Max Bartholdt, Simon F. G. Ehlers +3
Operating complex real-world systems, such as soft robots, can benefit from precise predictive control schemes that require accurate state and model knowledge. This knowledge is ty…
Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
Jan-Hendrik Ewering, Robin E. Herrmann, Niklas Wahlström +2
Embedding non-restrictive prior knowledge, such as energy conservation laws, into learning methods is a key motive to construct physically consistent dynamics models from limited d…
Dual Iterative Learning Control for Multiple-Input Multiple-Output Dynamics with Validation in Robotic Systems
Jan-Hendrik Ewering, Alessandro Papa, Simon F. G. Ehlers +2
Solving motion tasks autonomously and accurately is a core ability for intelligent real-world systems. To achieve genuine autonomy across multiple systems and tasks, key challenges…
Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge
Björn Volkmann, Jan-Hendrik Ewering, Michael Meindl +2
Accuracy and generalization capabilities are key objectives when learning dynamical system models. To obtain such models from limited data, current works exploit prior knowledge an…
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline
Jan-Hendrik Ewering, Björn Volkmann, Simon F. G. Ehlers +2
Intelligent real-world systems critically depend on expressive information about their system state and changing operation conditions, e.g., due to variation in temperature, locati…
Reliable State Estimation in a Truck-Semitrailer Combination using an Artificial Neural Network-Aided Extended Kalman Filter
Jan-Hendrik Ewering, Zygimantas Ziaukas, Simon F. G. Ehlers +1
Advanced driver assistance systems are critically dependent on reliable and accurate information regarding a vehicles' driving state. For estimation of unknown quantities, model-ba…
Integrated Model Predictive Control of High-Speed Railway Running Gears with Driven Independently Rotating Wheels
Jan-Hendrik Ewering, Christoph Schwarz, Simon F. G. Ehlers +3
Railway running gears with independently rotating wheels (IRW) can significantly improve wear figures, comfort, and safety in railway transportation, but certain measures for wheel…