Control Barrier Function

Control Barrier Functions (CBFs) are mathematical tools used to guarantee the safety of dynamical systems by ensuring that trajectories remain within a predefined "safe set," a key objective being to maximize the size of this safe set while respecting input constraints. Current research focuses on improving CBF's robustness to uncertainties (e.g., disturbances, noisy sensor data) and integrating them with various control architectures like Model Predictive Control (MPC) and reinforcement learning, often employing neural networks for adaptive parameter tuning or online CBF synthesis. This work is significant for enabling safe and reliable operation of autonomous systems in complex and unpredictable environments, with applications ranging from robotics and autonomous driving to aerospace.