Verification and Synthesis of Control Barrier Functions
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Abstract (arXiv)
Control systems often must satisfy strict safety requirements over an extended operating lifetime. Control Barrier Functions (CBFs) are a promising recent approach to constructing simple and safe control policies. This paper proposes a framework for verifying that a CBF guarantees safety for all time and synthesizing CBFs with verifiable safety in polynomial control systems. Our approach is to show that safety of CBFs is equivalent to the non-existence of solutions to a family of polynomial equations, and then prove that this nonexistence is equivalent to a pair of sum-of-squares constraints via the Positivstellensatz of algebraic geometry. We develop this Positivstellensatz to verify CBFs, as well as generalization to high-degree systems and multiple CBF constraints. We then propose a set of heuristics for CBF synthesis, including a general alternating-descent heuristic, a specialized approach for compact safe regions, and an approach for convex unsafe regions. Our approach is illustrated on two numerical examples.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Apr 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 3; 2025: 5; 2026: 1.
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- 2022Safe Control Under Input Limits with Neural Control Barrier Functionscited by 27
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
- 2023Exact Verification of ReLU Neural Control Barrier Functionscited by 14
- 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagationcited by 12
- 2023Synthesizing Stable Reduced-Order Visuomotor Policies for Nonlinear Systems via Sums-of-Squares Optimizationcited by 7
- 2025Learning Conservative Neural Control Barrier Functions from Offline Datacited by 4
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagationcited by 2
- 2026Safe Control using Learned Safety Filters and Adaptive Conformal Inferencecited by 2
- 2024Pre-Trained Vision Models as Perception Backbones for Safety Filters in Autonomous Drivingcited by 1
- 2024Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attackscited by 0
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 0
- 2025Sample Efficient Certification of Discrete-Time Control Barrier Functionscited by 0
- 2026Safe Control using Learned Safety Filters and Adaptive Conformal Inference20 Apr 2026
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation9 Nov 2025
- 2025Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning24 Oct 2025
- 2025Sample Efficient Certification of Discrete-Time Control Barrier Functions4 Sep 2025
- 2025Learning Conservative Neural Control Barrier Functions from Offline Data1 May 2025
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions20 Feb 2025
- 2024Pre-Trained Vision Models as Perception Backbones for Safety Filters in Autonomous Driving29 Oct 2024
- 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation4 Oct 2024
- 2024Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks28 Feb 2024
- 2023Exact Verification of ReLU Neural Control Barrier Functions13 Oct 2023
- 2023Synthesizing Stable Reduced-Order Visuomotor Policies for Nonlinear Systems via Sums-of-Squares Optimization24 Apr 2023
- 2022Safe Control Under Input Limits with Neural Control Barrier Functions20 Nov 2022
1 more
- 2022Safe Control Under Input Limits with Neural Control Barrier Functionscited by 27
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
- 2023Exact Verification of ReLU Neural Control Barrier Functionscited by 14
- 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagationcited by 12
- 2023Synthesizing Stable Reduced-Order Visuomotor Policies for Nonlinear Systems via Sums-of-Squares Optimizationcited by 7
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagationcited by 2
- 2026Safe Control using Learned Safety Filters and Adaptive Conformal Inferencecited by 2
- 2024Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attackscited by 0
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functionscited by 0
- 2025Sample Efficient Certification of Discrete-Time Control Barrier Functionscited by 0
- 2025Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learningcited by 0
- 2026Safe Control using Learned Safety Filters and Adaptive Conformal Inference20 Apr 2026
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation9 Nov 2025
- 2025Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning24 Oct 2025
- 2025Sample Efficient Certification of Discrete-Time Control Barrier Functions4 Sep 2025
- 2025Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions20 Feb 2025
- 2024Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation4 Oct 2024
- 2024Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks28 Feb 2024
- 2023Exact Verification of ReLU Neural Control Barrier Functions13 Oct 2023
- 2023Synthesizing Stable Reduced-Order Visuomotor Policies for Nonlinear Systems via Sums-of-Squares Optimization24 Apr 2023
- 2022Safe Control Under Input Limits with Neural Control Barrier Functions20 Nov 2022
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations18 Nov 2021