Adaptive Safety with Control Barrier Functions

TL;DR

This paper introduces adaptive Control Barrier Functions (aCBFs) to ensure safety in uncertain systems, unifying them with adaptive Control Lyapunov Functions within a QP framework, validated through adaptive cruise control simulations.

Contribution

It proposes a novel adaptive safety framework combining aCLFs and aCBFs for uncertain systems within a QP-based control methodology.

Findings

Successfully maintains safety in uncertain conditions

Achieves system stability and safety simultaneously

Validated through adaptive cruise control simulation

Abstract

Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunov-based methodology for stabilizing systems with parameter uncertainty. The goal of this paper is to revisit this classic formulation in the context of safety-critical control. This will motivate a variant of aCLFs in the context of safety: adaptive Control Barrier Functions (aCBFs). Our proposed approach adaptively achieves safety by keeping the systems state within a safe set even in the presence of parametric model uncertainty. We unify aCLFs and aCBFs into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework. We validate the ability of this unified framework to achieve stability and safety in an adaptive cruise control (ACC) simulation.