@article{MAKHILLJEAS202116919553,
    title = {Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation
Train},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {16},
    number = {9},
    pages = {278-281},
    year = {2021},
    issn = {1816-949x},
    doi = {jeasci.2021.278.281},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2021.278.281},
    author = {Mustefa,Eliyas and},
    keywords = {Maglev train,NARMA-L2 controller,model reference controller,predictive controller},
    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 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.}
    }