Learning for Safety-Critical Control with Control Barrier Functions
Abstract
Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains. Despite this success, model uncertainty remains a significant challenge in synthesizing safe controllers, leading to degradation in the properties provided by the controllers. This paper develops a machine learning framework utilizing Control Barrier Functions (CBFs) to reduce model uncertainty as it impact the safe behavior of a system. This approach iteratively collects data and updates a controller, ultimately achieving safe behavior. We validate this method in simulation and experimentally on a Segway platform.
Cite
Text
Taylor et al. "Learning for Safety-Critical Control with Control Barrier Functions." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Taylor et al. "Learning for Safety-Critical Control with Control Barrier Functions." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/taylor2020l4dc-learning/)BibTeX
@inproceedings{taylor2020l4dc-learning,
title = {{Learning for Safety-Critical Control with Control Barrier Functions}},
author = {Taylor, Andrew and Singletary, Andrew and Yue, Yisong and Ames, Aaron},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {708-717},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/taylor2020l4dc-learning/}
}