论文标题
使用控制屏障功能来学习可区分的安全 - 关键控制，以概括为新的环境
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
论文作者
论文摘要
控制障碍功能（CBF）已成为强制执行控制系统安全性的流行工具。 CBF通常用于二次程序公式（CBF-QP）作为安全 - 关键限制。通常需要手动调整CBF中的$ \ Mathcal {K} $功能，以平衡每个环境的性能和安全性之间的权衡。但是，这个过程通常是启发式的，对于高相对度系统可能会很棘手。此外，它可以防止CBF-QP在现实世界中推广到不同环境。通过将基于指数控制屏障函数的优化过程嵌入深度学习体系结构中的可区分层，我们提出了一个可区别的安全性控制控制框架，该框架有助于具有向前不相差的高度相对程度系统的新环境，以确保具有向前的高级系统的新环境。最后，我们在各种环境中使用2D双重和四倍积分系统验证了所提出的控制设计。
Control barrier functions (CBFs) have become a popular tool to enforce safety of a control system. CBFs are commonly utilized in a quadratic program formulation (CBF-QP) as safety-critical constraints. A class $\mathcal{K}$ function in CBFs usually needs to be tuned manually in order to balance the trade-off between performance and safety for each environment. However, this process is often heuristic and can become intractable for high relative-degree systems. Moreover, it prevents the CBF-QP from generalizing to different environments in the real world. By embedding the optimization procedure of the exponential control barrier function based quadratic program (ECBF-QP) as a differentiable layer within a deep learning architecture, we propose a differentiable safety-critical control framework that enables generalization to new environments for high relative-degree systems with forward invariance guarantees. Finally, we validate the proposed control design with 2D double and quadruple integrator systems in various environments.
