Learning Robust Hybrid Control Barrier Functions for Uncertain Systems

被引：18

作者：

Robey, Alexander ^{[1
]}

Lindemann, Lars ^{[1
]}

Tu, Stephen ^{[2
]}

Matni, Nikolai ^{[1
]}

机构：

[1] Univ Penn, Philadelphia, PA 19104 USA

[2] Google Brain Robot, New York, NY USA

来源：

IFAC PAPERSONLINE | 2021年 / 54卷 / 05期

关键词：

Control Barrier Functions; Robust Control; Hybrid Systems; Data-driven Control; LIMIT-CYCLES; SAFETY; STABILITY;

D O I：

10.1016/j.ifacol.2021.08.465

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty. Copyright (C) 2021 The Authors.

引用

页码：1 / 6

页数：6

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