Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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.
How to cite this article:
Mustefa Jibril, Eliyas Alemayehu Tadese and Messay Tadese. Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation
Train.
DOI: https://doi.org/10.36478/jeasci.2021.278.281
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2021.278.281