Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control
Abstract
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.
Cite
Text
Plaza et al. "Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.Markdown
[Plaza et al. "Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.](https://mlanthology.org/l4dc/2022/plaza2022l4dc-total/)BibTeX
@inproceedings{plaza2022l4dc-total,
title = {{Total Energy Shaping with Neural Interconnection and Damping Assignment - Passivity Based Control}},
author = {Plaza, Santiago Sanchez-Escalonilla and Reyes-Baez, Rodolfo and Jayawardhana, Bayu},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
pages = {520-531},
volume = {168},
url = {https://mlanthology.org/l4dc/2022/plaza2022l4dc-total/}
}