CBF Based

Control Barrier Functions (CBFs) are mathematical tools ensuring safety in dynamic systems by enforcing constraints on system trajectories, primarily addressing challenges in nonlinear systems with input limitations. Current research focuses on improving CBF performance and robustness through adaptive parameter tuning using methods like reinforcement learning, neural networks, and online optimization techniques, often incorporating uncertainty quantification and handling noisy inputs. This work is significant for enabling safe and reliable control in complex applications such as autonomous vehicles, robotics, and multi-agent systems, bridging the gap between theoretical safety guarantees and practical implementation.