Safe Control Under Input Limits with Neural Control Barrier Functions

Autor:	Liu, Simin, Liu, Changliu, Dolan, John
Rok vydání:	2022
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: CORL 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.11056 Zobrazit plný text záznamu View this record from Arxiv