入力制約下での安全制御のためのニューラル制御バリア関数
Safe Control Under Input Limits with Neural Control Barrier Functions
入力飽和を避けるニューラル制御バリア関数を学習者-批評家型アーキテクチャで合成し、高次元非線形システムの安全制御を実現した。
著者: Simin Liu, Changliu Liu, John Dolan
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations. In particular, our method is created for high-dimensional, general nonlinear systems, for which such tools are scarce. We leverage techniques from machine learning, like neural networks and deep learning, to simplify this challenging problem in nonlinear control design. The method consists of a learner-critic architecture, in which the critic gives counterexamples of input saturation and the learner optimizes a neural CBF to eliminate those counterexamples. We provide empirical results on a 10D state, 4D input quadcopter-pendulum system. Our learned CBF avoids input saturation and maintains safety over nearly 100% of trials.
