Direct decentralized neural control for nonlinear MIMO magnetic levitation system

Syuan Yi Chen; Faa Jeng Lin; Kuo Kai Shyu

doi:10.1016/j.neucom.2009.02.009

Direct decentralized neural control for nonlinear MIMO magnetic levitation system

Syuan Yi Chen, Faa Jeng Lin^*, Kuo Kai Shyu

^*Corresponding author for this work

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

29 Citations (Scopus)

Abstract

A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.

Original language	English
Pages (from-to)	3220-3230
Number of pages	11
Journal	Neurocomputing
Volume	72
Issue number	13-15
DOIs	https://doi.org/10.1016/j.neucom.2009.02.009
Publication status	Published - 2009 Aug
Externally published	Yes

Keywords

Decentralized control
Elman neural network
MIMO system
Magnetic levitation system

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2009.02.009

Cite this

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title = "Direct decentralized neural control for nonlinear MIMO magnetic levitation system",

abstract = "A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.",

keywords = "Decentralized control, Elman neural network, MIMO system, Magnetic levitation system",

author = "Chen, {Syuan Yi} and Lin, {Faa Jeng} and Shyu, {Kuo Kai}",

note = "Funding Information: The author would like to acknowledge the financial support of the National Science Council of Taiwan, ROC through its Grant NSC 95-2221-E-008-177-MY3. ",

year = "2009",

month = aug,

doi = "10.1016/j.neucom.2009.02.009",

language = "English",

volume = "72",

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journal = "Neurocomputing",

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T1 - Direct decentralized neural control for nonlinear MIMO magnetic levitation system

AU - Chen, Syuan Yi

AU - Lin, Faa Jeng

AU - Shyu, Kuo Kai

N1 - Funding Information: The author would like to acknowledge the financial support of the National Science Council of Taiwan, ROC through its Grant NSC 95-2221-E-008-177-MY3.

PY - 2009/8

Y1 - 2009/8

N2 - A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.

AB - A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.

KW - Decentralized control

KW - Elman neural network

KW - MIMO system

KW - Magnetic levitation system

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Direct decentralized neural control for nonlinear MIMO magnetic levitation system

Abstract

Keywords

ASJC Scopus subject areas

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