Safe Control Against Uncertainty: A Comprehensive Review of Control Barrier Function Strategies

The growing interest in robust designs and data-driven technologies for safe control problems underscores the critical need to understand uncertainty for ensuring reliable safety guarantees. This review offers a concise survey of recent advancements in the control barrier function (CBF) method, widely recognized as a principled and effective approach to safe control, particularly in the context of uncertainty. From a unified perspective, we classify uncertainty into three types based on their learnability and transferability. Then we explore the techniques associated with each type of uncertainty found in the existing literature. Additionally, we highlight a knowledge-based safe control framework that utilizes meta-learning techniques to address dynamic uncertainty, shedding light on the potential for future investigations into practical learning algorithms and control problems. Furthermore, we employ topic modeling technologies to identify and generalize topics from the literature, thus revealing research trends and ongoing real-world applications withing the scope of safe control.