# Risk-Aware Control of Discrete-Time Stochastic Systems: Integrating Kalman Filter and Worst-case CVaR in Control Barrier Functions

arXiv: https://arxiv.org/abs/2312.15638 · HTML: https://mlsanity.com/paper/2312.15638/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2023.

## Built on by (most cited first, 2 of 2)

- [Assumed Density Filtering and Smoothing with Neural Network Surrogate Models](https://mlsanity.com/paper/2511.09016/) (2025)
- [DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions](https://mlsanity.com/paper/2605.21257/) (2026)

## Abstract (arXiv)

This paper proposes control approaches for discrete-time linear systems subject to stochastic disturbances. It employs Kalman filter to estimate the mean and covariance of the state propagation, and the worst-case conditional value-at-risk (CVaR) to quantify the tail risk using the estimated mean and covariance. The quantified risk is then integrated into a control barrier function (CBF) to derive constraints for controller synthesis, addressing tail risks near safe set boundaries. Two optimization-based control methods are presented using the obtained constraints for half-space and ellipsoidal safe sets, respectively. The effectiveness of the obtained results is demonstrated using numerical simulations.