Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems   Linear dynamical systems are canonical models for learning-based control of
plants with uncertain dynamics. The setting consists of a stochastic
differential equation that captures the state evolution of the plant
understudy, while the true dynamics matrices are unknown and need to be learned
from the observed data of state trajectory. An important issue is to ensure
that the system is stabilized and destabilizing control actions due to model
uncertainties are precluded as soon as possible. A reliable stabilization
procedure for this purpose that can effectively learn from unstable data to
stabilize the system in a finite time is not currently available. In this work,
we propose a novel Bayesian learning algorithm that stabilizes unknown
continuous-time stochastic linear systems. The presented algorithm is flexible
and exposes effective stabilization performance after a remarkably short time
period of interacting with the system.

5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex