Safe Controller

Safe controller research focuses on designing control systems that guarantee the safety of dynamic systems, even under uncertainty or model inaccuracies. Current efforts concentrate on developing robust and efficient methods, such as control barrier functions (CBFs) and their variants (e.g., robust-adaptive CBFs), often integrated with machine learning techniques like neural networks and Gaussian processes, to ensure safety while maintaining performance. These advancements are crucial for deploying autonomous systems in safety-critical applications, including robotics, healthcare (e.g., robotic surgery), and energy systems, where reliable safety guarantees are paramount. The field is actively exploring ways to reduce conservatism and improve computational efficiency of safety verification and controller synthesis.