Application of Genetic Algorithm in Optimization of Fuzzy Control Rules

2006 ◽  
Author(s):  
Jingyuan Zhang ◽  
Yede Li
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Control Rules

Research on the Fuzzy PID Speed Control System of Permanent Magnet Linear Synchronous Motor Based on Genetic Algorithm

2014 ◽  
Vol 494-495 ◽  
pp. 1582-1586 ◽  
Author(s):  
Jun Liu ◽  
Qian Wei Xie
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Pid Control ◽  
Linear Time ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Fuzzy Pid Controller ◽  
Stability Performance ◽  
Control Rules ◽  
Genetic Algorithm Method

Focusing on the non-linear, time-varying, strong coupling and external load disturbance existing in PMLSM, a fuzzy PID controller based on genetic algorithms is designed to control the speed of PMLSM by absorbing the advantages of PID control and fuzzy control, and the genetic algorithm method is used to optimize fuzzy control rules. A simulation experiment was made to compare the effects of traditional PID control and fuzzy PID based on genetic algorithm control by Matlab. The simulation results verify that fuzzy PID control based on genetic algorithm is superior to PID control in dynamic stability performance and speed tracking power.

The Fuzzy PID Control of Resistance Furnace Temperature System Based on Genetic Algorithm

2013 ◽  
Vol 273 ◽  
pp. 678-682 ◽  
Author(s):  
Jing Yan Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Pid Control ◽  
Global Optimum ◽  
Fuzzy Pid ◽  
Matlab Simulation ◽  
Resistance Furnace ◽  
Fuzzy Pid Control ◽  
Furnace Temperature ◽  
Control Rules

The resistance furnace temperature system has low accuracy and big overshoots with fuzzy control. The fuzzy PID controller is used to optimize the resistance furnace temperature system, and the design scheme is developed. The fuzzy control and PID control are combined to control the system. If the system’s deviation is large the fuzzy control is adopted, else PID control is adopted. The genetic algorithm is adopted to train the controller’s membership functions, control rules and parameters. The global optimum of the controller’s parameters can be achieved. Matlab simulation results indicate that the resistance furnace temperature system with fuzzy PID control is more dynamic, robust, and highly precise.

Semi-Active Fuzzy Control of MR Damper on Structures by Genetic Algorithm

2009 ◽  
Vol 25 (1) ◽  
pp. N1-N6 ◽  
Author(s):  
Z.-S. Huang ◽  
C. Wu ◽  
D.-S. Hsu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Mr Damper ◽  
Flowing Liquid ◽  
New Device ◽  
Classic Problem ◽  
Control Rules ◽  
Nonlinear Force ◽  
Structural Responses ◽  
Semi Solid

AbstractThe magnetorheological (MR) damper is a new device proposed for structural protection. It is filled with MR fluid that can be changed, when exposed to a magnetic field, regularly from free flowing liquid, linear viscous one to semi-solid. A phenomenological model based on the Bouc-Wen hysteresis model is adopted to predict both the force-displacement behavior and the complex nonlinear force-velocity response. The theory of fuzzy control is adopted here to determine the command voltage of MR dampers, but the applying of fuzzy control rules has always to deal with the classic problem of optimization. And due to the structural responses of analysis results, it can be confirmed that the reducing effects have an obviously improvement after an optimization by genetic algorithm.

scholarly journals Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

2019 ◽  
Vol 19 (2) ◽  
pp. 87-103
Author(s):  
Gayane L. Beklaryan ◽  
Andranik S. Akopov ◽  
Nerses K. Khachatryan
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
System Dynamics ◽  
Performance Metrics ◽  
Parent Population ◽  
The Real ◽  
Control Rules ◽  
The Monte Carlo Method ◽  
Real Coded Genetic Algorithm ◽  
Dynamics Models

Abstract This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness from a potential extremum. Results of optimisation experiments demonstrate the greater time-efficiency of F-RCGA in comparison with other RCGAs, as well as the Monte-Carlo method. F-RCGA was validated by using well-known test instances and applied for the optimisation of characteristics of some system dynamics models.

Fuzzy Control of Printing Color Quality Based on Genetic Algorithm

2012 ◽  
Vol 472-475 ◽  
pp. 3071-3077
Author(s):  
Jun Jing Yang ◽  
Hong Yan Chu ◽  
Li Gang Cai ◽  
Lei Su
Keyword(s):  
Genetic Algorithm ◽  
Quality Control ◽  
Theoretical Model ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Working Environment ◽  
Printing Process ◽  
Control Rules ◽  
Color Quality ◽  
Controlled Object

Abstract : Aiming at the controlled object with large lag, model uncertainty and time variation due to the effects of working environment in printing process, and printing process requires few adjustment times, this paper designs a T-S fuzzy controller based on the theoretical model of printing color quality control, and uses the genetic algorithm to optimize the initial control rules of fuzzy controller . The optimization method aims at the problems of less known condition and the uncertain effects due to the environmental changes after the first printing. In the process of optimization, the theoretical model of the printing color quality control is used as the controlled object, and the parameters of control rule corresponding to the points of special error are optimized one by one, then the general fuzzy control rules can be got. Finally, an example illustrates the process of this method, and the robustness of the optimized fuzzy controller is analyzed. From the control results got by the optimized fuzzy controller, it can be seen that this method improves the control effects greatly, and reduces adjustment times. Finally, this paper gives some suggestions on its further perfection.

Fuzzy control based on genetic algorithm in intelligent psychology teaching system

2021 ◽  
pp. 1-9
Author(s):  
Xiaojia Pang ◽  
Yuwen Ning
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Control Theory ◽  
Classroom Teaching ◽  
Improved Genetic Algorithm ◽  
Group Function ◽  
Teaching Process ◽  
Teaching Management ◽  
Teaching System ◽  
Volume Group

The advancement of science has made computer technology and the education industry more and more closely related, and the development of intelligent teaching systems has also opened a new path for classroom teaching. This paper studies the application of fuzzy control based on genetic algorithms in the intelligent psychology teaching system. Facing the complicated variables in the teaching process, the improved genetic algorithm can better realize dynamic teaching decisions through fuzzy control. This article aims to improve the quality of psychology classroom teaching, and develops an intelligent psychology teaching system based on the fuzzy control theory of genetic algorithm. Combined with the current development of fuzzy control theory, the problems existing in the intelligent teaching system are studied and analyzed, and they have been optimized and improved. This paper proposes a control algorithm based on a teaching management system. The algorithm can implement fuzzy control on student models, knowledge organization structure, intelligent test papers and teaching decision-making. While restoring the real teaching process, it can better realize teaching students in accordance with their aptitude and improve teaching. The intelligence of the system. According to the system test data, the proportions of the difficulty of the system’s automatic test paper are 30.1%, 51.6%, 18.3%, which are in line with the designer’s set expectation of 3 : 5:2, which shows the improved genetic algorithm. It can realize the intelligent volume group function very well.

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