16 citations · 52 across the 16 of their papers we have counts for
Showing eess.SYShow all
2 papers · 1 filter
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…
eess.SY2023★ 10 cited
Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes
Tim-Lukas Habich, Sarah Kleinjohann, Moritz Schappler
Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions.…