By Topic

Generalized predictive control of a heat exchanger using fuzzy model

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

2 Author(s)
Matko, D. ; Fac. of Electr. Eng., Ljubljana Univ., Slovenia ; Kavsek-Blasizzo, K.

This paper proposes a new approach to predictive control of highly nonlinear processes based on the Takagi-Sugeno fuzzy model. It is shown how the Takagi-Sugeno fuzzy models can be linked to a special type of model based predictive control algorithm, the generalized predictive control (GPC). In GPC design, a purely linear transfer function model is used for long-range prediction. The advantage of GPC and other linear MBPC methods is the guaranteed convergence within each time sample, but they are not able to deal with strong process nonlinearities. In our approach, approximate linear models are extracted at each time sample by instantaneous linearization of the nonlinear fuzzy model, and adaptive GPC is used. Applicability of this approach to control a real world process (nonlinear laboratory-scale thermal plant) with operating point dependent gain and time constants is demonstrated in the paper

Published in:

Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE  (Volume:1 )

Date of Conference:

2000