An Embedded Fuzzy Agent for Online Tuning of a PID Controller for Position Control of a DC Motor

2013 ◽  
pp. 225-232 ◽  
Author(s):  
Luis A. Alvarado-Yañez ◽  
Luis M. Torres-Treviño ◽  
Angel Rodríguez-Liñán
Keyword(s):  
Pid Controller ◽  
Position Control ◽  
Dc Motor ◽  
Online Tuning

Optimum Setting of DC Drives PID Controller via Particle Swarm Optimization

2012 ◽  
Vol 157-158 ◽  
pp. 88-93 ◽  
Author(s):  
Guang Hui Chang ◽  
Jie Chang Wu ◽  
Chao Jie Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Position Control ◽  
Particle Swarm ◽  
Dc Motor ◽  
Motor Drive ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Simulation Results

In this paper, an intelligent controller of PM DC Motor drive is designed using particle swarm optimization (PSO) method for tuning the optimal proportional-integral-derivative (PID) controller parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency.To show the validity of the PID-PSO controller, a DC motor position control case is considered and some simulation results are shown. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment.. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies.

scholarly journals Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA

2018 ◽  
Vol 11 (3) ◽  
pp. 71-78
Author(s):  
Aula N. Abd
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Position Control ◽  
Back Propagation ◽  
Dc Motor ◽  
Motor Speed ◽  
Back Propagation Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Field Programmable

In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse neural network controller designed and implemented on Field Programmable Gate Array (FPGA) board. This NN is trained by Levenberg-Marquardt back propagation algorithm. After implementation on FPGA, the response appear completely the same as simulation response before implementation that mean the controller based on FPGA is very nigh to software designed controller. The controllers designed by both m-file and Simulink in MATLAB R2012a version 7.14.0.

scholarly journals Speed and Position Control of DC Motor using LabVIEW

2021 ◽  
Vol 9 (VIII) ◽  
pp. 111-116
Author(s):  
Sharad Gupta
Keyword(s):  
Pid Controller ◽  
Closed Loop ◽  
Position Control ◽  
Dc Motor ◽  
System Model ◽  
Normal Operation ◽  
Closed Loop System ◽  
Labview Software ◽  
Dc Motors ◽  
New Applications

New applications requiring both speed and position control have emerged as a result of technological advancements. In many applications, a DC motor's speed and position can be adjusted. The PID controller controls the speed and position of individually stimulated DC motors. Using LabVIEW software, this study attempts to control the speed and position of a DC motor as well as calculate the closed loop system model. Nonlinearity in any system has undesirable implications in normal operation. Unwanted outcomes including dead zones, saturation, and system reaction.

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