16 citations · 21 across the 11 of their papers we have counts for
5 papers · 1 filter
Time-Series Anomaly Detection for Mobile Robots in Automotive Active Safety Testing using an RNN-VAE
Henrik Meyer, Karsten Raguse, Armando Walter Colombo +2
Mobile robots, like the ultra-flat overrunable (UFO) robot platform, used in automotive active safety tests, currently lack self-diagnostic capabilities necessary to detect present…
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…
Predictive Energy Management for Recuperation Axles in Refrigerated Trailers
Dennis Bank, Simon F. G. Ehlers, Karl-Philipp Kortmann +3
Refrigerated truck trailers are currently mainly operated with environmentally harmful diesel units; an alternative is to operate the refrigeration unit with electrical energy. How…
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…
Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems
Henrik Krauss, Tim-Lukas Habich, Max Bartholdt +2
Physics-informed neural networks (PINNs) are trained using physical equations and can also incorporate unmodeled effects by learning from data. PINNs for control (PINCs) of dynamic…