By Topic

A Recurrent Fuzzy-Network-Based Inverse Modeling Method for a Temperature System Control

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)
Chia-Feng Juang ; Dept. of Electr. Eng., Nat. Chung-Hsing Univ., Taichung ; Jung-Shing Chen

Temperature control by a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) designed by modeling plant inverse is proposed in this paper. TRFN is a recurrent fuzzy network developed from a series of TSK-type fuzzy if--then rules, and is characterized by structure and parameter learning. In parameter learning, two types of learning algorithms, the Kalman filter and the gradient descent learning algorithms, are applied to consequent parameters depending on the learning situation. The TRFN has the following advantages when applied to temperature control problems: 1) high learning ability, which considerably reduces the controller training time; 2) no a priori knowledge of the plant order is required, which eases the design process; 3) good and robust control performance; 4) online learning ability, i.e., the TRFN can adapt itself to unpredictable plant changes. The TRFN-based direct inverse control configuration is applied to a real water bath temperature control plant, where various control conditions are experimented. The same experiments are also performed by proportional-integral (PI), fuzzy, and neural network controllers. From comparisons, the aforementioned advantages of a TRFN have been verified

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:37 ,  Issue: 3 )