By Topic

Nonlinear model predictive control using neural networks

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

4 Author(s)
Piche, S. ; Pavilion Technol., Austin, TX, USA ; Sayyar-Rodsari, B. ; Johnson, D. ; Gerules, M.

A neural-network-based technique for developing nonlinear dynamic models from empirical data for an model predictive control (MPC) algorithm is presented. These models can be derived for a wide variety of processes and can also be used efficiently in an MPC framework. The nonlinear MPC-based approach presented has been successfully implemented in a number of industrial applications in the refining, petrochemical, pulp and paper, power, and food industries. Performance of the controller on a nonlinear industrial process, a polyethylene reactor, and a simulated continuous stirred tank reactor is presented

Published in:

Control Systems, IEEE  (Volume:20 ,  Issue: 3 )