Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Enhancing statistical performance of data-driven controller tuning via L2-regularization
 
research article

Enhancing statistical performance of data-driven controller tuning via L2-regularization

Formentin, Simone  
•
Karimi, Alireza  
2014
Automatica

Noniterative data-driven techniques are design methods that allow optimal feedback control laws to be derived from input-output (I/O) data only, without the need of a model of the process. A drawback of these methods is that, in their standard formulation, they are not statistically efficient. In this paper, it is shown that they can be reformulated as L2-regularized optimization problems, by keeping the same assumptions and features, such that their statistical performance can be enhanced using the same identification data set. A convex optimization method is also introduced to find the regularization matrix. The proposed strategy is finally tested on a benchmark example in digital control system design.

  • Files
  • Details
  • Metrics
Type
research article
DOI
10.1016/j.automatica.2014.04.001
Web of Science ID

WOS:000336779100022

Author(s)
Formentin, Simone  
Karimi, Alireza  
Date Issued

2014

Publisher

Pergamon-Elsevier Science Ltd

Published in
Automatica
Volume

50

Issue

5

Start page

1514

End page

1520

Subjects

Data driven control

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LA  
Available on Infoscience
February 28, 2014
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/101241
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés