Robust Control Barrier Functions under High Relative Degree and Input Constraints for Satellite Trajectories
Not on the map. Why?
Abstract (arXiv)
This paper presents methodologies for constructing Control Barrier Functions (CBFs) for nonlinear, control-affine systems, in the presence of input constraints and bounded disturbances. More specifically, given a constraint function with high-relative-degree with respect to the system dynamics, the paper considers three methodologies, two for relative-degree 2 and one for higher relative-degrees, for creating CBFs whose zero sublevel sets are subsets of the constraint function's zero sublevel set. Three special forms of Robust CBFs (RCBFs) are developed as functions of the input constraints, system dynamics, and disturbance bounds, such that the resultant RCBF condition on the control input is always feasible for states in the RCBF zero sublevel set. The RCBF condition is then enforced in a switched fashion, which allows the system to operate safely without enforcing the RCBF condition when far from the safe set boundary and allows tuning of how closely trajectories approach the safe set boundary. The proposed methods are verified in simulations demonstrating the developed RCBFs in an asteroid flyby scenario for a satellite with low-thrust actuators, and in asteroid proximity operations for a satellite with high-thrust actuators.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 3; 2024: 0; 2025: 4; 2026: 1.
8 papers.
- 2023Exact Verification of ReLU Neural Control Barrier Functionscited by 14
- 2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidancecited by 8
- 2023Learning Predictive Safety Filter via Decomposition of Robust Invariant Setcited by 3
- 2025Neural Configuration-Space Barriers for Manipulation Planning and Controlcited by 3
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagationcited by 2
- 2025Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoidcited by 1
- 2025Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecastingcited by 0
- 2026Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operationscited by 0
- 2026Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations19 Mar 2026
- 2025Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation9 Nov 2025
- 2025Uncertainty Aware-Predictive Control Barrier Functions: Safer Human Robot Interaction through Probabilistic Motion Forecasting28 Aug 2025
- 2025Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid6 May 2025
- 2025Neural Configuration-Space Barriers for Manipulation Planning and Control6 Mar 2025
- 2023Learning Predictive Safety Filter via Decomposition of Robust Invariant Set12 Nov 2023
- 2023Exact Verification of ReLU Neural Control Barrier Functions13 Oct 2023
- 2023Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance6 Jul 2023