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Adaptive Safety with Control Barrier Functions

arXiv 1910.00555 ↗First posted 1 Oct 2019eess.SY cs.SYcited by 25

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Abstract (arXiv)

Adaptive Control Lyapunov Functions (aCLFs) were introduced 20 years ago, and provided a Lyapunov-based methodology for stabilizing systems with parameter uncertainty. The goal of this paper is to revisit this classic formulation in the context of safety-critical control. This will motivate a variant of aCLFs in the context of safety: adaptive Control Barrier Functions (aCBFs). Our proposed approach adaptively achieves safety by keeping the systems state within a safe set even in the presence of parametric model uncertainty. We unify aCLFs and aCBFs into a single control methodology for systems with uncertain parameters in the context of a Quadratic Program (QP) based framework. We validate the ability of this unified framework to achieve stability and safety in an adaptive cruise control (ACC) simulation.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 5; 2023: 2; 2024: 6; 2025: 5; 2026: 1.

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  1. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 337
  2. 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
  3. 2021Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertaintycited by 13
  4. 2021BarrierNet: A Safety-Guaranteed Layer for Neural Networkscited by 11
  5. 2023Online Control Barrier Functions for Decentralized Multi-Agent Navigationcited by 10
  6. 2021Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functionscited by 8
  7. 2024CBF-LLM: Safe Control for LLM Alignmentcited by 8
  8. 2024Learning Control Barrier Functions and their application in Reinforcement Learning: A Surveycited by 3
  9. 2024Implicit Safe Set Algorithm for Provably Safe Reinforcement Learningcited by 3
  10. 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functionscited by 2
  11. 2022Robust Action Governor for Uncertain Piecewise Affine Systems with Non-convex Constraints and Safe Reinforcement Learningcited by 1
  12. 2022Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functionscited by 1
13 more
  1. 2022Safe Reinforcement Learning with Probabilistic Control Barrier Functions for Ramp Mergingcited by 1
  2. 2023Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learningcited by 1
  3. 2024Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Modelscited by 1
  4. 2024Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigationcited by 1
  5. 2024Domain Adaptive Safety Filters via Deep Operator Learningcited by 1
  6. 2025Soft Actor-Critic-based Control Barrier Adaptation for Robust Autonomous Navigation in Unknown Environmentscited by 1
  7. 2021Safe Learning Reference Governor: Theory and Application to Fuel Truck Rollover Avoidancecited by 0
  8. 2022A stabilizing reinforcement learning approach for sampled systems with partially unknown modelscited by 0
  9. 2025Adaptive MPC-based quadrupedal robot control under periodic disturbancescited by 0
  10. 2025Safe Uncertainty-Aware Learning of Robotic Suturingcited by 0
  11. 2025Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structurescited by 0
  12. 2025Control Barrier Function for Aligning Large Language Modelscited by 0
  13. 2026Robust Taylor-Lagrange Control for Safety-Critical Systemscited by 0