ハイブリッドシステムのための局所制御バリア関数の学習
Learning Local Control Barrier Functions for Hybrid Systems
非線形ハイブリッド動的システムの安全性を保証するため、局所的な制御バリア関数（CBF）を学習し、安全なニューラルCBFベースの切替え制御器を構築する手法を提案。高次元自律レースを含むロボット例で有効性を示した。
著者: Shuo Yang, Yu Chen, Xiang Yin, George J. Pappas, Rahul Mangharam
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
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.
