By Topic

Worst-Case Identification of Errors-in-Variables Models in Closed Loop

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Li-Hui Geng ; Department of Automation, Tsinghua University, Beijing, China ; De-Yun Xiao ; Tao Zhang ; Jing-Yan Song

A worst-case identification method in frequency domain is proposed to cope with the identification of errors-in-variables models (EIVMs) in closed loop. With a priori bound for the disturbing noises of an EIVM in closed loop, a frequency-domain normalized coprime factor model (NCFM) with perturbation is derived and thus the identification of the EIVM becomes that of the NCFM. By employing the v-gap metric as an optimization criterion, the worst-case error for an identified nominal NCFM is easily quantified and the parameter optimization can be effectively solved by linear matrix inequalities (LMIs). During the parameter optimization, the derivative of the nominal NCFM is constrained to some degree to reduce the effect of overfitting phenomenon. Different from other EIVM identification methods, we use v-gap metric to characterize the disturbing noises and quantify the worst-case error for the nominal NCFM. As a result, the identification result is not a deterministic model but a model set. Moreover, this model set can be perfectly combined with the robust controller design. Finally, a numerical simulation is presented to verify the proposed method.

Published in:

IEEE Transactions on Automatic Control  (Volume:56 ,  Issue: 4 )