论文标题
从数据中学习混合控制屏障功能
Learning Hybrid Control Barrier Functions from Data
论文作者
论文摘要
由于缺乏系统的工具来获取混合系统安全控制法律的动机，我们提出了一个基于优化的框架，用于从数据中认证安全控制法律。特别是，我们假设系统动力学的设置并在其中提供了显示安全系统行为的数据。我们提出了混合系统的混合控制屏障功能，以综合安全控制输入。基于这个概念，我们提出了一个基于优化的框架，以从数据中学习此类混合控制障碍功能。重要的是，我们确定数据上的足够条件，以便优化问题的可行性确保了学习的混合控制屏障功能的正确性，从而确保了系统的安全性。我们在两项模拟研究中说明了我们的发现，包括指南针步态步行者。
Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is available. We propose hybrid control barrier functions for hybrid systems as a means to synthesize safe control inputs. Based on this notion, we present an optimization-based framework to learn such hybrid control barrier functions from data. Importantly, we identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned hybrid control barrier functions, and hence the safety of the system. We illustrate our findings in two simulations studies, including a compass gait walker.
