In this paper, we present a novel theoretical framework for online adaptation of Control Barrier Function (CBF) parameters, i.e., of the class K functions included in the CBF condition, under input constraints. We introduce the concept of locally validated CBF parameters, which are adapted online to guarantee finite-horizon safety, based on conditions derived from Nagumo's theorem and tangent cone analysis. To identify these parameters online, we integrate a learning-based approach with an uncertainty-aware verification process that account for both epistemic and aleatoric uncertainties inherent in neural network predictions. Our method is demonstrated on a VTOL quadplane model during challenging transition and landing maneuvers, showcasing enhanced performance while maintaining safety.

翻译：本文提出了一种新颖的理论框架，用于在输入约束下在线自适应控制屏障函数（CBF）参数，即CBF条件中包含的类K函数。我们引入了局部验证CBF参数的概念，这些参数基于Nagumo定理和切锥分析导出的条件在线自适应，以保证有限时间范围内的安全性。为在线识别这些参数，我们将基于学习的方法与不确定性感知验证过程相结合，该过程考虑了神经网络预测中固有的认知不确定性和随机不确定性。我们的方法在VTOL四旋翼飞机模型上进行验证，展示了在具有挑战性的过渡和着陆机动中增强性能的同时保持安全性。