By Topic

Computationally Efficient Identification of Global ARX Parameters With Guaranteed Stability

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

5 Author(s)
Ulaganathan Nallasivam ; Department of Chemical Engineering, Clarkson University, New York, USA ; Babki Srinivasan ; Vidyashankar Kuppuraj ; M. Nazmul Karim
more authors

Identification of stable parametric models from input-output data of a process (stable) is an essential task in system identification. For a stable process, the identified parametric model may be unstable due to one or more of the following reasons: 1) presence of noise in the measurements, 2) plant disturbances, 3) finite sample effects 4) over/under modeling of the process and 5) nonlinear distortions. Therefore, it is essential to impose stability conditions on the parameters during model estimation. In this technical note, we develop a computationally efficient approach for the identification of global ARX parameters with guaranteed stability. The computational advantage of the proposed approach is derived from the fact that a series of computationally tractable quadratic programming (QP) problems are solved to identify the globally optimal parameters. The importance of identifying globally optimal stable model parameters is high lighted through illustrative examples; this does not seem to have been discussed much in the literature.

Published in:

IEEE Transactions on Automatic Control  (Volume:56 ,  Issue: 6 )