%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 }