Input-to-State Safety With Control Barrier Functions
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Abstract (arXiv)
This letter presents a new notion of input-to-state safe control barrier functions (ISSf-CBFs), which ensure safety of nonlinear dynamical systems under input disturbances. Similar to how safety conditions are specified in terms of forward invariance of a set, input-to-state safety (ISSf) conditions are specified in terms of forward invariance of a slightly larger set. In this context, invariance of the larger set implies that the states stay either inside or very close to the smaller safe set; and this closeness is bounded by the magnitude of the disturbances. The main contribution of the letter is the methodology used for obtaining a valid ISSf-CBF, given a control barrier function (CBF). The associated universal control law will also be provided. Towards the end, we will study unified quadratic programs (QPs) that combine control Lyapunov functions (CLFs) and ISSf-CBFs in order to obtain a single control law that ensures both safety and stability in systems with input disturbances.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2018.
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Citing papers per year: 2021: 5; 2022: 3; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
10 papers.
- 2021Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overviewcited by 42
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrationscited by 19
- 2021Learning Robust Hybrid Control Barrier Functions for Uncertain Systemscited by 14
- 2021Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approachcited by 13
- 2022End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functionscited by 10
- 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functionscited by 2
- 2021Safe Learning of Uncertain Environmentscited by 0
- 2022Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Datacited by 0
- 2025Safe Uncertainty-Aware Learning of Robotic Suturingcited by 0
- 2026LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentialscited by 0
- 2026LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adversarial Potentials23 Sep 2026
- 2025Safe Uncertainty-Aware Learning of Robotic Suturing22 May 2025
- 2022End-to-End Imitation Learning with Safety Guarantees using Control Barrier Functions21 Dec 2022
- 2022Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data12 Dec 2022
- 2022Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions23 Aug 2022
- 2021Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations18 Nov 2021
- 2021Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview1 Oct 2021
- 2021Safe Learning of Uncertain Environments2 Mar 2021
- 2021Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach25 Feb 2021
- 2021Learning Robust Hybrid Control Barrier Functions for Uncertain Systems16 Jan 2021