10 citations · 29 across the 8 of their papers we have counts for
8 papers
Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF
Ricarda-Samantha Götte, Julia Timmermann
A major challenge in state estimation with model-based observers are low-quality models that lack of relevant dynamics. We address this issue by simultaneously estimating the syste…
Autonomous Golf Putting with Data-Driven and Physics-Based Methods
Annika Junker, Niklas Fittkau, Julia Timmermann +1
We are developing a self-learning mechatronic golf robot using combined data-driven and physics-based methods, to have the robot autonomously learn to putt the ball from an arbitra…
Adaptive Koopman-Based Models for Holistic Controller and Observer Design
Annika Junker, Keno Pape, Julia Timmermann +1
We present a method to obtain a data-driven Koopman operator-based model that adapts itself during operation and can be straightforwardly used for the controller and observer desig…
Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF
Ricarda-Samantha Götte, Julia Timmermann
State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscent…
Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
Oliver Schön, Ricarda-Samantha Götte, Julia Timmermann
While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard…
Learning Data-Driven PCHD Models for Control Engineering Applications
Annika Junker, Julia Timmermann, Ansgar Trächtler
The design of control engineering applications usually requires a model that accurately represents the dynamics of the real system. In addition to classical physical modeling, powe…