Predictive Control Barrier Functions for Online Safety Critical Control

TL;DR

This paper introduces a proactive control barrier function method that predicts and corrects system trajectories to ensure safety, reducing unnecessary interventions and computational load compared to traditional reactive methods.

Contribution

It proposes a systematic, predictive approach for constructing control barrier functions that consider future safety, improving over existing reactive techniques.

Findings

Proactive CBF reduces trajectory modifications.

Faster than nonlinear model predictive control.

Ensures safety with smaller control inputs.

Abstract

This paper presents a methodology for constructing Control Barrier Functions (CBFs) that proactively consider the future safety of a system along a nominal trajectory, and effect corrective action before the trajectory leaves a designated safe set. Specifically, this paper presents a systematic approach for propagating a nominal trajectory on a receding horizon, and then encoding the future safety of this trajectory into a CBF. If the propagated trajectory is unsafe, then a controller satisfying the CBF condition will modify the nominal trajectory before the trajectory becomes unsafe. Compared to existing CBF techniques, this strategy is proactive rather than reactive and thus potentially results in smaller modifications to the nominal trajectory. The proposed strategy is shown to be provably safe, and then is demonstrated in simulated scenarios where it would otherwise be difficult to construct a traditional CBF. In simulation, the predictive CBF results in less modification to the nominal trajectory and smaller control inputs than a traditional CBF, and faster computations than a nonlinear model predictive control approach.