| Autor: |
Sanchez Escalonilla Plaza, Santiago, Reyes-Báez, Rodolfo, Jayawardhana, Bayu, Firoozi, R., Mehr, N., Yel, E., Antonova, R., Bohg, J., Schwager, M., Kochenderfer, M. |
| Přispěvatelé: |
Discrete Technology and Production Automation, Robotics and image-guided minimally-invasive surgery (ROBOTICS) |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Zdroj: |
4th Annual Learning for Dynamics & Control Conference |
| Popis: |
In this work we exploit the universal approximation property of Neural Networks (NNs) to design interconnection and damping assignment (IDA) passivity-based control (PBC) schemes for fully-actuated mechanical systems in the port-Hamiltonian (pH) framework. To that end, we transform the IDA-PBC method into a supervised learning problem that solves the partial differential matching equations, and fulfills equilibrium assignment and Lyapunov stability conditions. A main consequence of this, is that the output of the learning algorithm has a clear control-theoretic interpretation in terms of passivity and Lyapunov stability. The proposed control design methodology is validated for mechanical systems of one and two degrees-of-freedom via numerical simulations. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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