Synthesis and Verification of Neural Control Barrier Functions for Safe Reinforcement Learning with Guarantees

While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through Certified Learning (CL), which combines a learning-based controller with formal methods to provide safety guarantees. This thesis...

Application of Control Barrier Functions to Collision Free Model Predictive Control: Robust UAV Trajectories with MPC-CBF and Euclidean Signed Distance Fields

Recent literature in real-time trajectory planning has proposed using Control Barrier Functions (CBFs) as collision constraints in Model Predictive Control (MPC) for efficient guidance, a concept referred to as MPC-CBF. This concept has been explored for both first and second-order CBFs. However, these approaches relied on an analytical...