# Disturbance Observer-based Robust Control Barrier Functions

arXiv: https://arxiv.org/abs/2203.12855 · HTML: https://mlsanity.com/paper/2203.12855/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [Learning Predictive Safety Filter via Decomposition of Robust Invariant Set](https://mlsanity.com/paper/2311.06769/) (2023)
- [Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Markets](https://mlsanity.com/paper/2510.04555/) (2025)
- [Learning Provable Neural Network Observer for Uncertain Dynamical Systems](https://mlsanity.com/paper/2609.30819/) (2026)

## Abstract (arXiv)

This work presents a safe control design approach that integrates the disturbance observer (DOB) and the control barrier function (CBF) for systems with external disturbances. Different from existing robust CBF results that consider the "worst case" of disturbances, this work utilizes a DOB to estimate and compensate for the disturbances. DOB-CBF-based controllers are constructed with provably safe guarantees by solving convex quadratic programs online, to achieve a better tradeoff between safety and performance. Two types of systems are considered individually depending on the magnitude of the input and disturbance relative degrees. The effectiveness of the proposed methods is illustrated via numerical simulations.