The fuzzy controller based on genetic algorithm of the high-voltage big-capacity AC motor soft starter

2011 ◽  
Author(s):  
Shihong Gan ◽  
Wei Gu ◽  
Jianxin Chu ◽  
Yuan yu
Keyword(s):  
Genetic Algorithm ◽  
High Voltage ◽  
Fuzzy Controller ◽  
Ac Motor ◽  
Soft Starter

Application of genetic algorithm to self-organizing fuzzy controller in fed-batch culture ofScutellaria baicalensis G. plant cell

1998 ◽  
Vol 15 (4) ◽  
pp. 404-410 ◽  
Author(s):  
Jeong-Woo Choi ◽  
Jin Man Cho ◽  
Jung Gun Lee ◽  
Won Hong Lee ◽  
Ik Hwan Kim ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Batch Culture ◽  
Plant Cell ◽  
Fuzzy Controller ◽  
Fed Batch ◽  
Fed Batch Culture ◽  
Self Organizing

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

A Research on the Fuzzy Control with Genetic Algorithm for Maximum Power Point Tracking of Photovoltaic Power System

2013 ◽  
Vol 303-306 ◽  
pp. 1153-1157
Author(s):  
Yi Zong Dai ◽  
De Jun Miao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Point ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Based on the nonlinear of the photovoltaic device output power and the frequent changes in the work environment, a fuzzy controller with genetic algorithm was applied to maximum power point tracking (MPPT) of photovoltaic generation system. The problem of difference in the different interval of the maximum point was solved. It ensure that the system has a higher accuracy .By comparing the method of the fuzzy control and the method of fuzzy control with genetic algorithm through simulation , the result demonstrates the better control effect.

scholarly journals Fuzzy Dynamic Adaptation of Gap Generation and Mutation in Genetic Optimization of Type 2 Fuzzy Controllers

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Leticia Cervantes ◽  
Oscar Castillo ◽  
Denisse Hidalgo ◽  
Ricardo Martinez-Soto
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Flight Control ◽  
Fuzzy Systems ◽  
Performance Enhancement ◽  
Fuzzy Controller ◽  
Control Problems ◽  
Genetic Optimization ◽  
Two Parameters

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.

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