TY  - JOUR
T1  - Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation
Train
AU - Jibril, Mustefa AU - Alemayehu Tadese, Eliyas AU - Tadese, Messay 
JO  - Journal of Engineering and Applied Sciences
VL  - 16
IS  - 9
SP  - 278
EP  - 281
PY  - 2021
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2021.278.281
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2021.278.281
KW  - Maglev train
KW  -NARMA-L2 controller
KW  -model reference controller
KW  -predictive controller
AB  - 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 study, 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.
ER  -