By Topic

System Identification using the Neural-Extended Kalman Filter for Control Modification

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
$31 $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)
Stubberud, S.C. ; Anzus Inc., San Diego

The neural extended Kalman filter has been shown to be able to work and train on-line in a control loop and as a state estimator for maneuver target tracking. Often, however, the design of a control system does not have a state estimator in the feedback loop. The ability of the NEKF to learn dynamics in an open-loop implementation, such as with target tracking and intercept prediction, can be used to identify mis-modeled dynamics. The improved system model can then be used to adapt the control law to provide better performance based on the actual system dynamics. This new approach to neural extended Kalman filter control operations is introduced in this work using applications to the nonlinear version of the standard cart-pendulum system.

Published in:

Neural Networks, 2006. IJCNN '06. International Joint Conference on

Date of Conference:

0-0 0