Learning for Safety-Critical Control with Control Barrier Functions
This addresses safety issues in control systems for applications like robotics, but it is incremental as it builds on existing CBF methods.
The paper tackles model uncertainty in safety-critical control by developing a machine learning framework that uses Control Barrier Functions to iteratively update controllers with collected data, achieving safe behavior validated in simulation and on a Segway platform.
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.