# A Robust, Efficient Predictive Safety Filter

arXiv: https://arxiv.org/abs/2311.08496 · HTML: https://mlsanity.com/paper/2311.08496/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2023.

## Built on by (most cited first, 1 of 1)

- [Topological Feasibility Guarantees for Differentiable Predictive Control](https://mlsanity.com/paper/2608.10332/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation. The proposed safety filter extends upon existing work to reject disturbances for discrete-time, time-varying nonlinear systems with time-varying constraints. The safety filter is based on novel concepts of robust, discrete-time barrier functions and can be used to filter any control law. Here, we use the safety filter in conjunction with Differentiable Predictive Control (DPC) as a promising offline learning-based policy optimization method. The approach is demonstrated on a two-tank system, building, and single-integrator example.