Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptation

TL;DR

This paper introduces Rate-Tunable Control Barrier Functions (RT-CBFs) that enable online adjustment of safety controller responses, improving system trajectories and safety margins in multi-agent systems.

Contribution

It proposes a novel RT-CBF framework with online parameter tuning, including conditions for multiple constraints and decentralized multi-agent applications.

Findings

RT-CBFs improve system response proximity to reference trajectories.

Online parameter adaptation reduces conservativeness in safety enforcement.

Decentralized trust-based parameter updates enhance multi-agent safety control.

Abstract

Control Barrier Functions offer safety certificates by dictating controllers that enforce safety constraints. However, their response depends on the classK function that is used to restrict the rate of change of the value of the barrier function along the system trajectories. This paper introduces the notion of a Rate-Tunable (RT) CBF, which allows for online tuning of the response of CBF-based controllers. In contrast to existing approaches that use a fixed classK function to ensure safety, we parameterize and adapt the classK function parameters online. We show that this helps improve the system's response in terms of the resulting trajectories being closer to a nominal reference while being sufficiently far from the boundary of the safe set. We provide point-wise sufficient conditions to be imposed on any user-given parameter dynamics so that multiple CBF constraints continue to admit a common control input with time. Finally, we introduce RT-CBF parameter dynamics for decentralized noncooperative multi-agent systems, where a trust factor, computed based on the instantaneous ease of constraint satisfaction, is used to update parameters online for a less conservative response.