An Overview on Safety-Critical Control Under Disturbances: A Control Barrier Function Approach

Abstract

Safety-critical control systems, such as autonomous vehicles and robotics, must operate reliably in complex, dynamic environments where ensuring both stability and strict adherence to safety constraints is essential. Here, safety refers to maintaining state forward invariance within a desired safe set. The control barrier function (CBF)-based safety-critical control (CBF-SC) has emerged as a promising framework, enabling control systems to maintain states within specified forward invariant sets. However, real-world applications often encounter undesirable disturbances and uncertainties, complicating the implementation of CBF and potentially compromising safety guarantees. This article provides a comprehensive survey of recent advances in CBF-based safety-critical control, with a particular focus on handling disturbances and uncertainties. The article begins with an exploration of the foundational principles of the control Lyapunov function and CBF, then reviews and compares strategies to improve robustness in CBF-SC systems, drawing on robust control, adaptive control, and disturbance/uncertainty estimation and attenuation. The comparison between CBF-SC and other related methods is also explored. Advanced mechatronic applications are also reviewed, showcasing how CBFs are implemented in autonomous vehicles, robotics, industrial automation systems, and energy platforms. Finally, future research directions are provided in the area to trigger further discussion and investigation.