# Safety-Critical Controller Synthesis with Reduced-Order Models

arXiv: https://arxiv.org/abs/2411.16479 · HTML: https://mlsanity.com/paper/2411.16479/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions](https://mlsanity.com/paper/2510.14959/) (2025)
- [Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models](https://mlsanity.com/paper/2606.09749/) (2026)
- [Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions](https://mlsanity.com/paper/2412.04658/) (2024)
- [Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy](https://mlsanity.com/paper/2605.15517/) (2026)

## Abstract (arXiv)

Reduced-order models (ROMs) provide lower dimensional representations of complex systems, capturing their salient features while simplifying control design. Building on previous work, this paper presents an overarching framework for the integration of ROMs and control barrier functions, enabling the use of simplified models to construct safety-critical controllers while providing safety guarantees for complex full-order models. To achieve this, we formalize the connection between full and ROMs by defining projection mappings that relate the states and inputs of these models and leverage simulation functions to establish conditions under which safety guarantees may be transferred from a ROM to its corresponding full-order model. The efficacy of our framework is illustrated through simulation results on a drone and hardware demonstrations on ARCHER, a 3D hopping robot.