# Robustness of Control Design via Bayesian Learning

arXiv: https://arxiv.org/abs/2205.06896 · HTML: https://mlsanity.com/paper/2205.06896/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 7 of 7)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Emergence of Locomotion Behaviours in Rich Environments](https://mlsanity.com/paper/1707.02286/) (2017)
- [Statistical Guarantees for the Robustness of Bayesian Neural Networks](https://mlsanity.com/paper/1903.01980/) (2019)
- [Bayesian Inference with Certifiable Adversarial Robustness](https://mlsanity.com/paper/2102.05289/) (2021)
- [Robustness Guarantees for Bayesian Inference with Gaussian Processes](https://mlsanity.com/paper/1809.06452/) (2018)
- [Safe Control under Uncertainty](https://mlsanity.com/paper/1510.07313/) (2015)
- [Structured learning of safety guarantees for the control of uncertain dynamical systems](https://mlsanity.com/paper/2112.03347/) (2021)

## Abstract (arXiv)

In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search task. We seek to find a linear controller that stabilizes a one-dimensional open-loop unstable stochastic system. We compare two methods to deduce the controller: the first (deterministic) one assumes perfect knowledge of system parameter and state, the second takes into account uncertainties in both and employs Bayesian learning to compute a posterior distribution for the controller.