Hybrid dynamical systems are ubiquitous as practical robotic applications often involve both continuous states and discrete switchings. Safety is a primary concern for hybrid robotic systems. Existing safety-critical control approaches for hybrid systems are either computationally inefficient, detrimental to system performance, or limited to small-scale systems. To amend these drawbacks, in this paper, we propose a learning-enabled approach to construct local Control Barrier Functions (CBFs) to guarantee the safety of a wide class of nonlinear hybrid dynamical systems. The end result is a safe neural CBF-based switching controller. Our approach is computationally efficient, minimally invasive to any reference controller, and applicable to large-scale systems. We empirically evaluate our framework and demonstrate its efficacy and flexibility through two robotic examples including a high-dimensional autonomous racing case, against other CBF-based approaches and model predictive control.

翻译：混合动力系统在机器人应用中普遍存在，因为实际应用通常涉及连续状态和离散切换。安全性是混合机器人系统的首要关注点。现有的混合系统安全关键控制方法要么计算效率低下，要么对系统性能有害，要么仅限于小规模系统。为了修正这些缺点，本文提出一种基于学习的方法来构建局部控制屏障函数，以保证一大类非线性混合动力系统的安全性。最终成果是一个基于神经CBF的安全切换控制器。我们的方法计算高效，对任何参考控制器的侵入性最小，并且适用于大规模系统。我们通过两个机器人实例（包括一个高维自主赛车案例）与其他基于CBF的方法和模型预测控制进行对比，实证评估了我们的框架，并证明了其有效性和灵活性。