Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions

Understand

Learning-based control has recently shown great efficacy in performing complex tasks for various applications.

• However, to deploy it in real systems, it is of vital importance to guarantee the system will stay safe.
• Control Barrier Functions (CBFs) offer mathematical tools for designing safety-preserving controllers for systems with known dynamics.
• In this article, we first introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers using Gaussian Process (GP) regression to close the gap between an approximate mathematical model and the real system, which results in a second-order cone program (SOCP)-based control design.