By Topic

An approximate-predictor approach to reduced-order models and controllers for distributed-parameter systems

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

1 Author(s)
R. P. Leland ; Dept. of Electr. Eng., Alabama Univ., Tuscaloosa, AL, USA

Two reduced-order digital controllers for distributed parameter systems (DPS) are described. Reduced-order models approximate the optimal finite past predictor and error covariance for the full system to minimize an approximation to the Kullback-Leibler information distance (KLID). An LQG controller based on a reduced-order-system model is described. A reduced-order controller is found to minimize the KLID between the closed-loop system outputs with the full- and reduced-order controllers. Noncollocated control of a flexible beam is simulated

Published in:

IEEE Transactions on Automatic Control  (Volume:44 ,  Issue: 3 )