Safe Control Under Input Limits with Neural Control Barrier Functions

Simin Liu, Changliu Liu, John Dolan
Proceedings of The 6th Conference on Robot Learning, PMLR 205:1970-1980, 2023.

Abstract

We propose new methods to synthesize control barrier function (CBF) based safe controllers that avoid input saturation, which can cause safety violations. In particular, our method is created for high-dimensional, general nonlinear systems, for which such tools are scarce. We leverage techniques from machine learning, like neural networks and deep learning, to simplify this challenging problem in nonlinear control design. The method consists of a learner-critic architecture, in which the critic gives counterexamples of input saturation and the learner optimizes a neural CBF to eliminate those counterexamples. We provide empirical results on a 10D state, 4D input quadcopter-pendulum system. Our learned CBF avoids input saturation and maintains safety over nearly 100% of trials.

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-liu23e, title = {Safe Control Under Input Limits with Neural Control Barrier Functions}, author = {Liu, Simin and Liu, Changliu and Dolan, John}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {1970--1980}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/liu23e/liu23e.pdf}, url = {https://proceedings.mlr.press/v205/liu23e.html}, abstract = {We propose new methods to synthesize control barrier function (CBF) based safe controllers that avoid input saturation, which can cause safety violations. In particular, our method is created for high-dimensional, general nonlinear systems, for which such tools are scarce. We leverage techniques from machine learning, like neural networks and deep learning, to simplify this challenging problem in nonlinear control design. The method consists of a learner-critic architecture, in which the critic gives counterexamples of input saturation and the learner optimizes a neural CBF to eliminate those counterexamples. We provide empirical results on a 10D state, 4D input quadcopter-pendulum system. Our learned CBF avoids input saturation and maintains safety over nearly 100% of trials. } }
Endnote
%0 Conference Paper %T Safe Control Under Input Limits with Neural Control Barrier Functions %A Simin Liu %A Changliu Liu %A John Dolan %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-liu23e %I PMLR %P 1970--1980 %U https://proceedings.mlr.press/v205/liu23e.html %V 205 %X We propose new methods to synthesize control barrier function (CBF) based safe controllers that avoid input saturation, which can cause safety violations. In particular, our method is created for high-dimensional, general nonlinear systems, for which such tools are scarce. We leverage techniques from machine learning, like neural networks and deep learning, to simplify this challenging problem in nonlinear control design. The method consists of a learner-critic architecture, in which the critic gives counterexamples of input saturation and the learner optimizes a neural CBF to eliminate those counterexamples. We provide empirical results on a 10D state, 4D input quadcopter-pendulum system. Our learned CBF avoids input saturation and maintains safety over nearly 100% of trials.
APA
Liu, S., Liu, C. & Dolan, J.. (2023). Safe Control Under Input Limits with Neural Control Barrier Functions. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:1970-1980 Available from https://proceedings.mlr.press/v205/liu23e.html.

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