Auxiliary-Variable Adaptive Control Barrier Functions

TL;DR

This paper introduces Auxiliary-Variable Adaptive Control Barrier Functions (AVCBFs), a new framework that improves safety and feasibility in control systems by dynamically adjusting constraints and tuning parameters, especially under challenging bounds.

Contribution

The paper proposes AVCBFs with auxiliary variables and an automatic tuning method to enhance feasibility and safety in CBF-based control, addressing limitations of existing approaches.

Findings

AVCBFs reduce infeasibility in safety-critical control tasks.

The approach outperforms existing CBF methods in adaptive cruise control.

Demonstrated effectiveness in obstacle avoidance scenarios.

Abstract

This paper addresses the challenge of ensuring safety and feasibility in control systems using Control Barrier Functions (CBFs). Existing CBF-based Quadratic Programs (CBF-QPs) often encounter feasibility issues due to mixed relative degree constraints, input nullification problems, and the presence of tight or time-varying control bounds, which can lead to infeasible solutions and compromised safety. To address these challenges, we propose Auxiliary-Variable Adaptive Control Barrier Functions (AVCBFs), a novel framework that introduces auxiliary variables in auxiliary functions to dynamically adjust CBF constraints without the need of excessive additional constraints. The AVCBF method ensures that all components of the control input explicitly appear in the desired-order safety constraint, thereby improving feasibility while maintaining safety guarantees. Additionally, we introduce an automatic tuning method that iteratively adjusts AVCBF hyperparameters to ensure feasibility and safety with less conservatism. We demonstrate the effectiveness of the proposed approach in adaptive cruise control and obstacle avoidance scenarios, showing that AVCBFs outperform existing CBF methods by reducing infeasibility and enhancing adaptive safety control under tight or time-varying control bounds.