Backup Control Barrier Functions: Formulation and Comparative Study
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Abstract (arXiv)
The backup control barrier function (CBF) was recently proposed as a tractable formulation that guarantees the feasibility of the CBF quadratic programming (QP) via an implicitly defined control invariant set. The control invariant set is based on a fixed backup policy and evaluated online by forward integrating the dynamics under the backup policy. This paper is intended as a tutorial of the backup CBF approach and a comparative study to some benchmarks. First, the backup CBF approach is presented step by step with the underlying math explained in detail. Second, we prove that the backup CBF always has a relative degree 1 under mild assumptions. Third, the backup CBF approach is compared with benchmarks such as Hamilton Jacobi PDE and Sum-of-Squares on the computation of control invariant sets, which shows that one can obtain a control invariant set close to the maximum control invariant set under a good backup policy for many practical problems.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 4; 2026: 3.
10 papers.
- 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 77
- 2022Safe Control Under Input Limits with Neural Control Barrier Functionscited by 27
- 2025Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoningcited by 10
- 2025Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Controlcited by 7
- 2026Provably Safe, Yet Scalable Reinforcement Learningcited by 1
- 2022Geometry of Radial Basis Neural Networks for Safety Biased Approximation of Unsafe Regionscited by 0
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 0
- 2025Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecastingcited by 0
- 2026LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentialscited by 0
- 2026Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Controlcited by 0
- 2026Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Control28 Sep 2026
- 2026LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials23 Sep 2026
- 2026Provably Safe, Yet Scalable Reinforcement Learning12 Jun 2026
- 2025Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting28 Aug 2025
- 2025Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning15 May 2025
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions20 Feb 2025
- 2025Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control5 Feb 2025
- 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems11 Sep 2023
- 2022Safe Control Under Input Limits with Neural Control Barrier Functions20 Nov 2022
- 2022Geometry of Radial Basis Neural Networks for Safety Biased Approximation of Unsafe Regions11 Oct 2022