Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train

Report and Opinion Journal 12 (5):21-25 (2020)
Download Edit this record How to cite View on PhilPapers
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.
Categories
No categories specified
(categorize this paper)
PhilPapers/Archive ID
JIBCON-3
Upload history
Archival date: 2020-09-20
View other versions
Added to PP index
2020-09-20

Total views
122 ( #38,726 of 2,438,934 )

Recent downloads (6 months)
18 ( #35,081 of 2,438,934 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.