# Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems

arXiv: https://arxiv.org/abs/1905.06978 · HTML: https://mlsanity.com/paper/1905.06978/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 6 of 6)

- [Stabilizing Dynamical Systems via Policy Gradient Methods](https://mlsanity.com/paper/2110.06418/) (2021)
- [Joint Learning-Based Stabilization of Multiple Unknown Linear Systems](https://mlsanity.com/paper/2201.01387/) (2022)
- [Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems](https://mlsanity.com/paper/2112.15094/) (2021)
- [Safe Linear-Quadratic Dual Control with Almost Sure Performance Guarantee](https://mlsanity.com/paper/2103.13278/) (2021)
- [Reinforcement Learning Policies in Continuous-Time Linear Systems](https://mlsanity.com/paper/2109.07630/) (2021)
- [Regret Analysis of Certainty Equivalence Policies in Continuous-Time Linear-Quadratic Systems](https://mlsanity.com/paper/2206.04434/) (2022)

## Abstract (arXiv)

Data-driven control strategies for dynamical systems with unknown parameters are popular in theory and applications. An essential problem is to prevent stochastic linear systems becoming destabilized, due to the uncertainty of the decision-maker about the dynamical parameter. Two randomized algorithms are proposed for this problem, but the performance is not sufficiently investigated. Further, the effect of key parameters of the algorithms such as the magnitude and the frequency of applying the randomizations is not currently available. This work studies the stabilization speed and the failure probability of data-driven procedures. We provide numerical analyses for the performance of two methods: stochastic feedback, and stochastic parameter. The presented results imply that as long as the number of statistically independent randomizations is not too small, fast stabilization is guaranteed.