Optimal Design of Type1 TSK Fuzzy Controller Using GRLA for AVR System

A new methodology for designing optimal systematic GA-based fuzzy controller is presented in this paper. Our design is based on genetic reinforcement learning algorithm (GRLA), unlike normal GA, GRLA is only based on the competition and cooperation among chromosomes for survival. The proposed method...

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Bibliographic Details
Main Authors: Naderi, F., Gharaveisi, A.A., Rashidinejad, M.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Automatic control
Biological cells
Control systems
Design methodology
Fuzzy control
Fuzzy systems
Genetics
Learning
Optimal control
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Summary:A new methodology for designing optimal systematic GA-based fuzzy controller is presented in this paper. Our design is based on genetic reinforcement learning algorithm (GRLA), unlike normal GA, GRLA is only based on the competition and cooperation among chromosomes for survival. The proposed method tries to find best chromosomes with good combination to form an optimal fuzzy controller. GRLA as design method has been applied to the automatic voltage regulator (AVR) system. The controller is able to follow the input reference. A Mamdani controller designated via an ordinary GA is applied using five membership functions for inputs and output variables. A PID controller designed by Ziegler-Nichols method is also implemented to the same problem. A comparison between the obtained results shows the effectiveness of the proposed GRLA method.
DOI:10.1109/LESCPE.2007.4437362