# A Robust, Multiple Control Barrier Function Framework for Input Constrained Systems

arXiv: https://arxiv.org/abs/2205.13726 · HTML: https://mlsanity.com/paper/2205.13726/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2022.

## Built on by (most cited first, 6 of 6)

- [Safe Control Under Input Limits with Neural Control Barrier Functions](https://mlsanity.com/paper/2211.11056/) (2022)
- [How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints](https://mlsanity.com/paper/2511.18606/) (2025)
- [Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach](https://mlsanity.com/paper/2208.02319/) (2022)
- [Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments](https://mlsanity.com/paper/2402.16449/) (2024)
- [Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions](https://mlsanity.com/paper/2502.15006/) (2025)
- [Neural Network-assisted Interval Reachability for Systems with Control Barrier Function-Based Safe Controllers](https://mlsanity.com/paper/2504.08249/) (2025)

## Abstract (arXiv)

We propose a novel (Type-II) zeroing control barrier function (ZCBF) for safety-critical control, which generalizes the original ZCBF approach. Our method allows for applications to a larger class of systems (e.g. passivity-based) while still ensuring robustness, for which the construction of conventional ZCBFs is difficult. We also propose a locally Lipschitz continuous control law that handles multiple ZCBFs, while respecting input constraints, which is not currently possible with existing ZCBF methods. We apply the proposed concept for unicycle navigation in an obstacle-rich environment.