论文标题
一种学习线性控制屏障功能的迭代方法
An Iterative Method to Learn a Linear Control Barrier Function
论文作者
论文摘要
控制屏障功能（CBF）最近开始作为开发控制系统安全要求的方法的基础。但是，为通用系统构建此类功能是一项非平凡的任务。本文提出了一个基于迭代的，基于优化的框架，以从给定的用户指定的集合设置为一般控制仿射系统获得CBF。在不失去一般性的情况下，我们将CBF参数化为状态的一组线性函数。通过从给定用户指定的集合中获取样品，我们将学习CBF学习到解决线性功能系数的优化问题的问题。生成的线性功能构建了CBF并产生具有正向不变属性的安全集。此外，提出的框架明确地解决了CBF构建过程中的控制输入约束。通过学习非线性摩尔Greitzer喷气发动机的CBF来证明所提出的方法的有效性，该发动机可以防止系统轨迹进入不安全集合。
Control barrier function (CBF) has recently started to serve as a basis to develop approaches for enforcing safety requirements in control systems. However, constructing such function for a general system is a non-trivial task. This paper proposes an iterative, optimization-based framework to obtain a CBF from a given user-specified set for a general control affine system. Without losing generality, we parameterize the CBF as a set of linear functions of states. By taking samples from the given user-specified set, we reformulate the problem of learning a CBF into an optimization problem that solves for linear function coefficients. The resulting linear functions construct the CBF and yield a safe set which has forward invariance property. In addition, the proposed framework explicitly addresses control input constraints during the construction of CBFs. Effectiveness of the proposed method is demonstrated by learning a CBF for an nonlinear Moore Greitzer jet engine, where the system trajectory is prevented from entering unsafe set.
