Abstract
Feedforward control of a manipulator can be generated with a sufficiently accurate stable inverse model of the manipulator. A Feedforward Neural Network (FNN) can be trained with experimental data to generate feedforward control without knowledge about the system at hand. However, the FNN output can show unphysical behaviour especially in operational regimes where the training data is sparse. We consider including a Lagrangian Neural Network (LNN) that is expected to predict the (inverse) multibody system behaviour more robustly.
| Original language | English |
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| Pages | 1 |
| Number of pages | 2 |
| Publication status | Published - 18 Jul 2022 |
| Event | IUTAM Symposium on Optimal Design and Control of Multibody Systems 2022: Adjoint Methods, Alternatives, and Beyond - Hamburg University of Technology (TUHH), Hamburg, Germany Duration: 18 Jul 2022 → 21 Jul 2022 https://www.tuhh.de/mum/iutam-symposium-2022.html |
Conference
| Conference | IUTAM Symposium on Optimal Design and Control of Multibody Systems 2022 |
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| Country/Territory | Germany |
| City | Hamburg |
| Period | 18/07/22 → 21/07/22 |
| Internet address |