Minimizing Conservatism in Safety-Critical Control for Input-Delayed Systems via Adaptive Delay Estimation

TL;DR

This paper introduces an adaptive safety control framework that reduces conservatism in delay-aware control barrier functions for input-delayed systems, improving safety guarantees in autonomous vehicles.

Contribution

It develops an online adaptive method to tighten delay estimation bounds, decreasing conservatism in delay adaptive control barrier functions for safety-critical systems.

Findings

Reduces conservatism of delay adaptive control barrier functions.

Ensures the maximum state prediction error bound is non-increasing.

Validated on an automated connected truck system.

Abstract

Input delays affect systems such as teleoperation and wirelessly autonomous connected vehicles, and may lead to safety violations. One promising way to ensure safety in the presence of delay is to employ control barrier functions (CBFs), and extensions thereof that account for uncertainty: delay adaptive CBFs (DaCBFs). This paper proposes an online adaptive safety control framework for reducing the conservatism of DaCBFs. The main idea is to reduce the maximum delay estimation error bound so that the state prediction error bound is monotonically non-increasing. To this end, we first leverage the estimation error bound of a disturbance observer to bound the state prediction error. Second, we design two nonlinear programs to update the maximum delay estimation error bound satisfying the prediction error bound, and subsequently update the maximum state prediction error bound used in DaCBFs. The proposed method ensures the maximum state prediction error bound is monotonically non-increasing, yielding less conservatism in DaCBFs. We verify the proposed method in an automated connected truck application, showing that the proposed method reduces the conservatism of DaCBFs.