Magnetic levitation system is operated primarily based at the principle
of magnetic attraction and repulsion to
levitate the passengers and the train. However, magnetic levitation
trains are rather nonlinear and open loop
unstable which makes it hard to govern. In this paper, investigation,
design and control of a nonlinear Maglev train
based on NARMA-L2, model reference and predictive controllers. The
response of the Maglev train with the
proposed controllers for the precise role of a Magnetic levitation
machine have been as compared for a step input
signal. The simulation consequences prove that the Maglev teach system
with NARMA-L2 controller suggests the
quality performance in adjusting the precise function of the system
and the device improves the experience
consolation and street managing criteria.
:C\:\\Users\\user\\Desktop\\Mustefa Jibril Publication to Dire Dawa University\\Publication with Certificate\\Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train\\36298 Paper.pdf:PDF
%0 Journal Article
%1 MustefaJibril12020q
%A Mustefa Jibril1, Eliyas Alemayehu Tadese2
%D 2020
%J Report and Opinion Journal
%K Maglev NARMA-L2 controller controller, model predictive reference train,
%N 5
%P 21-25
%R 10.7537/marsroj120520.04
%T Comparison of Neural Network NARMA-L2 Model Reference
and Predictive Controllers for Nonlinear EMS Magnetic
Levitation Train
%V 12
%X Magnetic levitation system is operated primarily based at the principle
of magnetic attraction and repulsion to
levitate the passengers and the train. However, magnetic levitation
trains are rather nonlinear and open loop
unstable which makes it hard to govern. In this paper, investigation,
design and control of a nonlinear Maglev train
based on NARMA-L2, model reference and predictive controllers. The
response of the Maglev train with the
proposed controllers for the precise role of a Magnetic levitation
machine have been as compared for a step input
signal. The simulation consequences prove that the Maglev teach system
with NARMA-L2 controller suggests the
quality performance in adjusting the precise function of the system
and the device improves the experience
consolation and street managing criteria.
@article{MustefaJibril12020q,
abstract = {Magnetic levitation system is operated primarily based at the principle
of magnetic attraction and repulsion to
levitate the passengers and the train. However, magnetic levitation
trains are rather nonlinear and open loop
unstable which makes it hard to govern. In this paper, investigation,
design and control of a nonlinear Maglev train
based on NARMA-L2, model reference and predictive controllers. The
response of the Maglev train with the
proposed controllers for the precise role of a Magnetic levitation
machine have been as compared for a step input
signal. The simulation consequences prove that the Maglev teach system
with NARMA-L2 controller suggests the
quality performance in adjusting the precise function of the system
and the device improves the experience
consolation and street managing criteria.},
added-at = {2020-10-07T16:07:29.000+0200},
author = {Mustefa Jibril1, Eliyas Alemayehu Tadese2},
biburl = {https://www.bibsonomy.org/bibtex/285711234fdd36a93d0cf5fd156179c11/mustefa1981},
doi = {10.7537/marsroj120520.04},
file = {:C\:\\Users\\user\\Desktop\\Mustefa Jibril Publication to Dire Dawa University\\Publication with Certificate\\Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train\\36298 Paper.pdf:PDF},
interhash = {6694211203368570168508469fdeb3e2},
intrahash = {85711234fdd36a93d0cf5fd156179c11},
journal = {Report and Opinion Journal},
keywords = {Maglev NARMA-L2 controller controller, model predictive reference train,},
month = May,
number = 5,
owner = {user},
pages = {21-25},
review = {peer reviwed},
timestamp = {2020-10-07T16:08:26.000+0200},
title = {Comparison of Neural Network NARMA-L2 Model Reference
and Predictive Controllers for Nonlinear EMS Magnetic
Levitation Train},
volume = 12,
year = 2020
}