Lyapunov Barrier Function

Lyapunov barrier functions (LBFs) are mathematical tools used in control systems to guarantee safety and stability while achieving desired objectives, such as reaching a goal state or tracking a trajectory. Current research focuses on extending LBFs to handle increasingly complex scenarios, including constrained optimal control problems, robot navigation with obstacles and human-robot interaction, often employing techniques like model predictive control, reinforcement learning, and adaptive neural control. This work is significant because it enables the design of safe and reliable controllers for robots and other dynamic systems operating in uncertain or constrained environments, with applications ranging from autonomous vehicles to industrial automation.