A modified self-tuning fuzzy logic temperature controller for metal induction heating

2020 ◽  
Vol 91 (6) ◽  
pp. 064905 ◽  
Author(s):  
Chia-Jung Chang ◽  
Tung-Hua Chiang ◽  
Cheng-Chi Tai
Keyword(s):  
Fuzzy Logic ◽  
Induction Heating ◽  
Temperature Controller ◽  
Self Tuning ◽  
Metal Induction

scholarly journals Optimamization of Laguerre Estimation for Temperature Controller Self-tuning

2007 ◽  
Vol 127 (5) ◽  
pp. 540-541
Author(s):  
Hiroto Hamane ◽  
Yoshikazu Hyodo ◽  
Yoichi Hayashi ◽  
Kazuyoshi Miyazaki
Keyword(s):  
Temperature Controller ◽  
Self Tuning

scholarly journals Experimental simplified rule of self tuning fuzzy logic-model reference adaptive speed controller for induction motor drive

2020 ◽  
Vol 20 (3) ◽  
pp. 1653
Author(s):  
M.Z. Ismail ◽  
M.H.N. Talib ◽  
Z. Ibrahim ◽  
J. Mat Lazi ◽  
Z. Rasin
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Adaptive Controller ◽  
Tuning Parameter ◽  
Model Reference ◽  
Wide Range ◽  
Speed Performance ◽  
Self Tuning ◽  
Model Reference Adaptive

<span>Fuzzy logic controller (FLC) has shown excellent performance in dealing with the non-linearity and complex dynamic model of the induction motor. However, a conventional constant parameter FLC (CPFL) will not be able to provide–good coverage performance for a wide speed range operation with a single tuning parameter. Therefore, this paper proposed a self tuning mechanism FLC approach by model reference adaptive controller (ST-MRAC) to continuously allow to adjust the parameters. Due to real time hardware application, the dominant rules selection method for simplified rules has been implemented as part of the reducing computational burden. Experiment results validate a good performance of the ST-MRAC compared to the CPFL for the   speed performance in terms of the wide range of operations and disturbance showed remarkable performance.</span>

Sign in / Sign up

Export Citation Format

Share Document