Disturbance Observer-based Robust Control Barrier Functions
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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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Mar 2022.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2023Learning Predictive Safety Filter via Decomposition of Robust Invariant Setcited by 3
- 2025Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Marketscited by 0
- 2026Learning Provable Neural Network Observer for Uncertain Dynamical Systemscited by 0
- 2026Learning Provable Neural Network Observer for Uncertain Dynamical Systems25 Sep 2026
- 2025Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Markets6 Oct 2025
- 2023Learning Predictive Safety Filter via Decomposition of Robust Invariant Set12 Nov 2023