Application of Fuzzy Logic Controller with Self-Tuning PID Parameters for Contral System of 40kg Coke Oven

2013 ◽  
Author(s):  
Qingsi Zhang ◽  
Jinfeng Bai
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Coke Oven ◽  
Self Tuning ◽  
Tuning Pid Parameters

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>

Self Tuning Based Adaptive Fuzzy Logic Controller in Lab view for Sterilizing Equipments

2014 ◽  
Vol 3 (2) ◽  
pp. 84
Author(s):  
P. J. Ragu
Keyword(s):  
Fuzzy Logic ◽  
Temperature Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Lab View ◽  
Adaptive Fuzzy Logic ◽  
Self Tuning ◽  
Input Variables ◽  
Temperature Errors

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning  Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

Free vibration control of smart composite beams using particle swarm optimized self-tuning fuzzy logic controller

2014 ◽  
Vol 333 (21) ◽  
pp. 5244-5268 ◽  
Author(s):  
Nemanja D. Zorić ◽  
Aleksandar M. Simonović ◽  
Zoran S. Mitrović ◽  
Slobodan N. Stupar ◽  
Aleksandar M. Obradović ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Free Vibration ◽  
Vibration Control ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Composite Beams ◽  
Smart Composite ◽  
Self Tuning

scholarly journals Self-tuning Fuzzy Logic Controller Based on Takagi-Sugeno Applied to Induction Motor Drives

2018 ◽  
Vol 9 (4) ◽  
pp. 1967 ◽  
Author(s):  
Nabil Farah ◽  
M. H. N. Talib ◽  
Z. Ibrahim ◽  
J. M. Lazi ◽  
Maaspaliza Azri
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Experimental Testing ◽  
Motor Drives ◽  
Computational Burden ◽  
Response Characteristics ◽  
Speed Error ◽  
Self Tuning ◽  
Takagi Sugeno

<span>Fuzzy logic controller has been the main focus for many researchers and industries in motor drives. The popularity of Fuzzy Logic Controller (FLC) is due to its reliability and ability to handle parameters changes during load or disturbance. Fuzzy logic design can be visualized in two categories, mamdani design or Takagi-Sugeno (TS). Mamdani type can facilitate the design process, however it require high computational burden especially with big number of rules and experimental testing. This paper, develop Self-Tuning (ST) mechanism based on Takagi-Sugeno (TS) fuzzy type. The mechanism tunes the input scaling factor of speed fuzzy control of Induction Motor (IM) drives Based on the speed error and changes of error. A comparison study is done between the standard TS and the ST-TS based on simulations approaches considering different speed operations. Speed response characteristics such as rise time, overshoot, and settling time are compared for ST-TS and TS. It was shown that ST-TS has optimum results compared to the standard TS. The significance of the proposed method is that, optimum computational burden reduction is achieved.</span>

Development of a self-tuning fuzzy logic controller

1993 ◽  
Vol 6 (2) ◽  
pp. 119-131 ◽  
Author(s):  
J.A. Meech ◽  
L.A. Jordon
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Self Tuning
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