Control Lyapunov Function

Control Lyapunov functions (CLFs) are mathematical tools used to design stabilizing controllers for nonlinear systems, ensuring both stability and safety. Current research focuses on integrating CLFs with other techniques like control barrier functions (CBFs) to guarantee safety, employing neural networks and sum-of-squares programming for efficient CLF construction and verification, and leveraging CLFs within reinforcement learning frameworks to improve sample efficiency and robustness. This work is significant because it enables the development of more reliable and efficient control systems for complex applications, such as robotics and autonomous driving, by providing provable guarantees of stability and safety in the presence of uncertainty and constraints.