Safety Critical Control

Safety-critical control focuses on designing controllers that guarantee the safe operation of systems, particularly in situations where failures could have severe consequences. Current research emphasizes robust methods, such as control barrier functions (CBFs) and their variants (e.g., high-order CBFs, disturbance-robust CBFs), often integrated with model predictive control (MPC) or reinforcement learning (RL) frameworks, to handle uncertainties and complex constraints. These techniques are applied across diverse domains, including robotics, aerospace, and autonomous vehicles, improving safety and reliability in increasingly complex and dynamic environments. The development of efficient and verifiable safety guarantees is a key focus, driving advancements in both theoretical understanding and practical applications.