Self-generating rule-mapping fuzzy controller design using a genetic algorithm

2002 ◽  
Vol 149 (2) ◽  
pp. 143-148 ◽  
Author(s):  
C.-C. Chen ◽  
C.-C. Wong
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Controller ◽  
Controller Design

scholarly journals Study on Hybrid Fuzzy Controller Design by Model Reduction Method

2018 ◽  
Vol 7 (3.34) ◽  
pp. 558
Author(s):  
Joon Ho Cho ◽  
. .
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Performance Index ◽  
Delay Time ◽  
Pid Controller ◽  
Control Parameter ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Design Method ◽  
Reduced Model

Background/Objectives: In this paper, we proposed an improved model shrinking method and a hybrid-smith prediction fuzzy control design using a reduced model.Methods/Statistical analysis: The method of model reduction is based on Nyquist curve of frequency response, and the reduced model is obtained by considering the response of the transient state and the response of the steady state to the method. The proposed hybrid-smith prediction fuzzy controller tuning method was able to obtain the parameter value by utilizing a reduced model and using a genetic algorithm.Findings: The optimum PID controller design method using the reduced model applied a reduced model and a Smith prediction structure that compensates for the delay time in order to improve the control performance, and as a result, in order to minimize the performance index ITAE value I was able to design a controller. Here, the value of the control parameter was used by combining a method of directly obtaining the reduced model and a method of using the genetic algorithm by numerical analysis. In conclusion, the design method of the hybrid-smith Fuzzy control is a method of controlling by combining the PID controller, the Smith prediction compensating the delay time and the fuzzy controller in parallel, the value of the PID control parameter is optimized using the reduced model The value of the PID parameter was determined. The conversion coefficients (GE, GD, GH, GC) of Fuzzy control were obtained by applying genetic algorithm.Improvements/Applications: In the proposed method, it is possible to obtain directly the value of the parameter of the optimum PID controller and the value of the Smith prediction by using the reduced model, and the part of the Fuzzy conversion coefficient can be obtained by using the genetic algorithm , The ITAE performance index improved more than the conventional method.  

Study of Fuzzy Control for Intelligent Cantilever Beam Based on Genetic Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 25-30 ◽  
Author(s):  
Jing Jun Zhang ◽  
Xiao Pin Guo ◽  
Li Li He ◽  
Rui Zhen Gao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Cantilever Beam ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Fitness Function ◽  
Fuzzy Rules ◽  
Control Effect ◽  
The Core ◽  
Simulation Results

The design of fuzzy controller is the key of fuzzy control system, while the core of fuzzy controller design lies in fuzzy rules, whose performance determines the control effect of fuzzy system. General fuzzy rules are obtained from expert experience, in which much subjectivity exists. In this paper, a fuzzy controller is designed by taking an intelligent cantilever beam as the research object. And a method using the genetic algorithm to optimize fuzzy rules is proposed and the genetic coding as well as the fitness function is confirmed. Finally, the simulation model of intelligent cantilever beam is built by Matlab/Simulink, and the vibration control effects of fuzzy controller optimized by genetic algorithm are compared with those un-optimized. The simulation results indicate that the vibration amplitude of intelligent cantilever beam has a significant decrease and the vibration decay rate has a significant increase after the fuzzy rules optimized.

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