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.

Extended version (12 Pages), Short version submitted to Learning for Dynamics & Control (L4DC) 2020 Conference

References in corpus (1)

• Safe Interactive Model-Based Learning

Cited by in corpus (4)

• Neural Certificates for Safe Control Policies
• Learning Stability Certificates from Data
• Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach
• Learning Safe Neural Network Controllers with Barrier Certificates