Acquisition of fuzzy control rules for a mobile robot using genetic algorithm

2000 ◽  
Author(s):  
H. Kawanaka ◽  
T. Yoshikawa ◽  
S. Tsuruoka
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Control ◽  
Mobile Robot ◽  
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.

Identificaion of Fuzzy Control Rules Utilizing Genetic Algorithms and its Application to Mobile Robot

1992 ◽  
Vol 25 (20) ◽  
pp. 249-254
Author(s):  
Hee Soo Hwang ◽  
Young Hoon Joo ◽  
Hyun Ki Kim ◽  
Kwang Bang Woo
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Control ◽  
Mobile Robot ◽  
Control Rules

scholarly journals Multivariable Fuzzy Control Based Mobile Robot Odor Source Localization via Semitensor Product

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ping Jiang ◽  
Yuzhen Wang ◽  
Aidong Ge
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Mobile Robot ◽  
Source Localization ◽  
Odor Source ◽  
Algebraic Equations ◽  
Fuzzy Control System ◽  
Control Rules ◽  
Control Scheme ◽  
Set Up

In order to take full advantage of the multisensor information, a MIMO fuzzy control system based on semitensor product (STP) is set up for mobile robot odor source localization (OSL). Multisensor information, such as vision, olfaction, laser, wind speed, and direction, is the input of the fuzzy control system and the relative searching strategies, such as random searching (RS), nearest distance-based vision searching (NDVS), and odor source declaration (OSD), are the outputs. Fuzzy control rules with algebraic equations are given according to the multisensor information via STP. Any output can be updated in the proposed fuzzy control system and has no influence on the other searching strategies. The proposed MIMO fuzzy control scheme based on STP can reach the theoretical system of the mobile robot OSL. Experimental results show the efficiency of the proposed method.

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.

Sign in / Sign up

Export Citation Format

Share Document