Contraction L1-Adaptive Control Using Gaussian Processes
Abstract
We present a control framework that enables safe simultaneous learning and control for systems subject to uncertainties. The two main constituents are contraction theory-based $\mathcal{L}_1$-adaptive ($\mathcal{CL}_1$) control and Bayesian learning in the form of Gaussian process (GP) regression. The $\mathcal{CL}_1$ controller ensures that control objectives are met while providing safety certificates. Furthermore, the controller incorporates any available data into GP models of uncertainties, which improves performance and enables the motion planner to achieve optimality safely. This way, the safe operation of the system is always guaranteed, even during the learning transients.
Cite
Text
Gahlawat et al. "Contraction L1-Adaptive Control Using Gaussian Processes." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Gahlawat et al. "Contraction L1-Adaptive Control Using Gaussian Processes." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/gahlawat2021l4dc-contraction/)BibTeX
@inproceedings{gahlawat2021l4dc-contraction,
title = {{Contraction L1-Adaptive Control Using Gaussian Processes}},
author = {Gahlawat, Aditya and Lakshmanan, Arun and Song, Lin and Patterson, Andrew and Wu, Zhuohuan and Hovakimyan, Naira and Theodorou, Evangelos A.},
booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
year = {2021},
pages = {1027-1040},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/gahlawat2021l4dc-contraction/}
}