Tracking a feature point in profile data using an articulated object model and tuning parameters using the genetic algorithm-applied to an arc-welding robot system

2003 ◽  
Author(s):  
Y. Kusachi ◽  
K. Kato ◽  
M. Okudaira
Keyword(s):  
Genetic Algorithm ◽  
Arc Welding ◽  
Feature Point ◽  
Object Model ◽  
Welding Robot ◽  
Profile Data ◽  
Robot System ◽  
Tuning Parameters

scholarly journals Trends of Sensor-based Intelligent Arc Welding Robot System

2014 ◽  
Vol 20 (10) ◽  
pp. 1051-1056
Author(s):  
Ji Hoon Joung ◽  
Hyeon-Ho Shin ◽  
Young Hoon Song ◽  
SooJong Kim
Keyword(s):  
Arc Welding ◽  
Welding Robot ◽  
Robot System

PSO-Based Tunning of PID Controller for Speed Control of DC Motor

2014 ◽  
Vol 7 (3) ◽  
pp. 65-79
Author(s):  
Ibrahem S. Fatah
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dc Motor ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Tuning Parameters ◽  
Pid Controller Tuning ◽  
Rising Time

In this paper, a Proportional-Integral-Derivative (PID) controller of DC motor is designed by using particle swarm optimization (PSO) strategy for formative optimal PID controller tuning parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Comparing with conventional PID controller using Genetic Algorithm, the planned method is more proficient in improving the speed loop response stability, the steady state error is reduced, the rising time is perfected and the change of the required input do not affect the performances of driving motor with no overtaking.

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