Learning for Safety-Critical Control with Control Barrier Functions

Andrew Taylor; Andrew Singletary; Yisong Yue; Aaron Ames

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arXiv · 1912.10099

Learning for Safety-Critical Control with Control Barrier Functions

Andrew Taylor·Andrew Singletary·Yisong Yue·Aaron Ames

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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.

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BibTeX RIS

Andrew Taylor, Andrew Singletary, Yisong Yue, Aaron Ames. 2019-12-20. Learning for Safety-Critical Control with Control Barrier Functions. https://arxiv.org/abs/1912.10099

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