Learning Robust State Observers Using Neural ODEs
Abstract
Relying on recent research results on Neural ODEs, this paper presents a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberger-like observers and their nonlinear extension (Kazantzis-Kravaris-Luenberger (KKL) observers) for systems with partially-known nonlinear dynamics and fully unknown nonlinear dynamics, respectively. In particular, for tuneable KKL observers, the relationship between the design of the observer and its trade-off between convergence speed and robustness is analysed and used as a basis for improving the robustness of the learning-based observer in training. We illustrate the advantages of this approach in numerical simulations.
Cite
Text
Miao and Gatsis. "Learning Robust State Observers Using Neural ODEs." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.Markdown
[Miao and Gatsis. "Learning Robust State Observers Using Neural ODEs." Proceedings of The 5th Annual Learning for Dynamics and Control Conference, 2023.](https://mlanthology.org/l4dc/2023/miao2023l4dc-learning/)BibTeX
@inproceedings{miao2023l4dc-learning,
title = {{Learning Robust State Observers Using Neural ODEs}},
author = {Miao, Keyan and Gatsis, Konstantinos},
booktitle = {Proceedings of The 5th Annual Learning for Dynamics and Control Conference},
year = {2023},
pages = {208-219},
volume = {211},
url = {https://mlanthology.org/l4dc/2023/miao2023l4dc-learning/}
}