Modeling and control with neural networks for a magnetic levitation system

Jose de Jesus Rubio; Lixian Zhang; Edwin Lughofer; Panuncio Cruz; Ahmed Alsaedi; Tasawar Hayet

doi:10.1016/j.neucom.2016.09.101

Modeling and control with neural networks for a magnetic levitation system

Jose de Jesus Rubio, Lixian Zhang, Edwin Lughofer, Panuncio Cruz, Ahmed Alsaedi, Tasawar Hayet

Institute for Mathematical Methods in Medicine and Data Based Modeling

Research output: Contribution to journal › Article › peer-review

Abstract

This study presents the model and control of the magnetic levitation system. The model considers the angular position of the ball, also a neural network approximates the electromagnetic parameter. The neural network controller is the combination of a nonlinear method and a neural network, also its stability is guaranteed by utilizing the Lyapunov method. The proposed controller is compared with the two stages controller for the trajectory tracking in the magnetic levitation system.

Original language	English
Pages (from-to)	113-121
Number of pages	9
Journal	Neurocomputing
Volume	227
DOIs	https://doi.org/10.1016/j.neucom.2016.09.101
Publication status	Published - 2016

Fields of science

101 Mathematics
101013 Mathematical logic
101024 Probability theory
102001 Artificial intelligence
102003 Image processing
102019 Machine learning
603109 Logic
202027 Mechatronics

JKU Focus areas

Computation in Informatics and Mathematics
Mechatronics and Information Processing
Nano-, Bio- and Polymer-Systems: From Structure to Function

Access to Document

10.1016/j.neucom.2016.09.101

Cite this

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title = "Modeling and control with neural networks for a magnetic levitation system",

abstract = "This study presents the model and control of the magnetic levitation system. The model considers the angular position of the ball, also a neural network approximates the electromagnetic parameter. The neural network controller is the combination of a nonlinear method and a neural network, also its stability is guaranteed by utilizing the Lyapunov method. The proposed controller is compared with the two stages controller for the trajectory tracking in the magnetic levitation system.",

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AU - Rubio, Jose de Jesus

AU - Zhang, Lixian

AU - Lughofer, Edwin

AU - Cruz, Panuncio

AU - Alsaedi, Ahmed

AU - Hayet, Tasawar

PY - 2016

Y1 - 2016

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AB - This study presents the model and control of the magnetic levitation system. The model considers the angular position of the ball, also a neural network approximates the electromagnetic parameter. The neural network controller is the combination of a nonlinear method and a neural network, also its stability is guaranteed by utilizing the Lyapunov method. The proposed controller is compared with the two stages controller for the trajectory tracking in the magnetic levitation system.

U2 - 10.1016/j.neucom.2016.09.101

DO - 10.1016/j.neucom.2016.09.101

M3 - Article

SN - 1872-8286

VL - 227

SP - 113

EP - 121

JO - Neurocomputing

JF - Neurocomputing

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