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  4. Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output Feedback Systems
 
conference paper

Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output Feedback Systems

Zheng, Yang
•
Furieri, Luca
•
Kamgarpour, Maryam  
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May 29, 2021
Proceedings of the 3rd Conference on Learning for Dynamics and Control
Learning for Dynamics and Control

This paper studies a class of partially observed Linear Quadratic Gaussian (LQG) problems with unknown dynamics. We establish an end-to-end sample complexity bound on learning a robust LQG controller for open-loop stable plants. This is achieved using a robust synthesis procedure, where we first estimate a model from a single input-output trajectory of finite length, identify an H-infinity bound on the estimation error, and then design a robust controller using the estimated model and its quantified uncertainty. Our synthesis procedure leverages a recent control tool called Input-Output Parameterization (IOP) that enables robust controller design using convex optimization. For open-loop stable systems, we prove that the LQG performance degrades linearly with respect to the model estimation error using the proposed synthesis procedure. Despite the hidden states in the LQG problem, the achieved scaling matches previous results on learning Linear Quadratic Regulator (LQR) controllers with full state observations.

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Type
conference paper
Author(s)
Zheng, Yang
Furieri, Luca
Kamgarpour, Maryam  
Li, Na
Date Issued

2021-05-29

Publisher

PMLR

Published in
Proceedings of the 3rd Conference on Learning for Dynamics and Control
Start page

559

End page

570

URL
https://proceedings.mlr.press/v144/zheng21b.html
Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
SYCAMORE  
Event nameEvent date
Learning for Dynamics and Control

2021-05-29

Available on Infoscience
December 1, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/183438
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