不确定系统学习强力混合控制障碍功能 (Learning Robust Hybrid Control Barrier Functions for Uncertain Systems)

The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.

翻译：在安全关键应用中,需要强有力的控制法律特别重要。我们提议强有力的混合控制屏障功能,作为综合确保稳健安全的控制法律的一种手段。基于这一概念,我们为从数据中学习稳健的混合控制屏障功能提出了一个优化问题。我们确定了数据上的充分条件,以便优化问题的可行性能够确保学到的稳健混合控制屏障功能的正确性。我们的技术使我们能够安全地扩大指南针行走器的吸引区域,而指南针行走器会受到模式不确定性的影响。