# Safe Control Synthesis with Uncertain Dynamics and Constraints

arXiv: https://arxiv.org/abs/2202.09557 · HTML: https://mlsanity.com/paper/2202.09557/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2022.

## Built on by (most cited first, 7 of 7)

- [SafeFlowMatcher: Safe and Fast Planning using Flow Matching with Control Barrier Functions](https://mlsanity.com/paper/2509.24243/) (2025)
- [Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes](https://mlsanity.com/paper/2405.08756/) (2024)
- [DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety](https://mlsanity.com/paper/2407.00616/) (2024)
- [Distributionally Robust Policy and Lyapunov-Certificate Learning](https://mlsanity.com/paper/2404.03017/) (2024)
- [Safe Learning Control with Optimality and Stability Guarantees](https://mlsanity.com/paper/2501.15373/) (2025)
- [Geometry of Radial Basis Neural Networks for Safety Biased Approximation of Unsafe Regions](https://mlsanity.com/paper/2210.05596/) (2022)
- [Worst-Case Distance-Aware Error Bounds for Neural Networks](https://mlsanity.com/paper/2510.22021/) (2025)

## Abstract (arXiv)

This paper considers safe control synthesis for dynamical systems with either probabilistic or worst-case uncertainty in both the dynamics model and the safety constraints. We formulate novel probabilistic and robust (worst-case) control Lyapunov function (CLF) and control barrier function (CBF) constraints that take into account the effect of uncertainty in either case. We show that either the probabilistic or the robust (worst-case) formulation leads to a second-order cone program (SOCP), which enables efficient safe and stable control synthesis. We evaluate our approach in PyBullet simulations of an autonomous robot navigating in unknown environments and compare the performance with a baseline CLF-CBF quadratic programming approach.