3 papers
eess.SY2026
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…
cs.RO2025
Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory
Johann Licher, Max Bartholdt, Henrik Krauss +3
Dynamic control of soft continuum robots (SCRs) holds great potential for expanding their applications, but remains a challenging problem due to the high computational demands of a…
eess.SY2024
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…