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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 (Unpublished)

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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 Paper
Taylor, Andrew J.0000-0002-5990-590X
Singletary, Andrew0000-0001-6635-4256
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:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101301
Deposited By: George Porter
Deposited On:14 Feb 2020 21:00
Last Modified:03 Aug 2020 21:14

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