CaltechAUTHORS
  A Caltech Library Service

Learning for Safety-Critical Control with Control Barrier Functions

Taylor, Andrew J. and Singletary, Andrew and Yue, Yisong and Ames, Aaron D. (2019) Learning for Safety-Critical Control with Control Barrier Functions. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200214-105558873

[img] PDF - Submitted Version
See Usage Policy.

1845Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200214-105558873

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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1912.10099arXivDiscussion Paper
ORCID:
AuthorORCID
Yue, Yisong0000-0001-9127-1989
Ames, Aaron D.0000-0003-0848-3177
Additional Information:© 2020 A.J. Taylor, A. Singletary, Y. Yue & A.D. Ames. Extended version (12 Pages), Short version submitted to Learning for Dynamics & Control (L4DC) 2020 Conference.
Record Number:CaltechAUTHORS:20200214-105558873
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200214-105558873
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101301
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:14 Feb 2020 21:00
Last Modified:14 Feb 2020 21:00

Repository Staff Only: item control page