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A modeling and control approach to magnetic levitation system based on state-dependent ARX model

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A modeling and control approach to magnetic levitation system based on state-dependent ARX model. / Qin, Yemei; Peng, Hui; Ruan, Wenjie; Wu, Jun; Gao, Jiacheng.

In: Journal of Process Control, Vol. 24, No. 1, 01.01.2014, p. 93-112.

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Qin, Yemei ; Peng, Hui ; Ruan, Wenjie ; Wu, Jun ; Gao, Jiacheng. / A modeling and control approach to magnetic levitation system based on state-dependent ARX model. In: Journal of Process Control. 2014 ; Vol. 24, No. 1. pp. 93-112.

Bibtex

@article{f7fbd9c0ebf74f829218047870ca35ab,
title = "A modeling and control approach to magnetic levitation system based on state-dependent ARX model",
abstract = "Magnetic levitation (Maglev) systems are usually strongly nonlinear, open-loop unstable and fast responding. In order to control the position of the steel ball in a Maglev system, a data-driven modeling approach and control strategy is presented in this paper. A state-dependent AutoRegressive with eXogenous input (SD-ARX) model is built to represent the dynamic behavior between the current of electromagnetic coil and the position of the ball. State-dependent functional coefficients of the SD-ARX model are approximated by Gaussian radial basis function (RBF) neural networks. The model parameters are identified offline by applying the structured nonlinear parameter optimization method (SNPOM). Based on the model, a predictive controller is designed to stabilize the magnetic levitation ball to a given position or to make it track a desired trajectory. The real-time control results of the proposed approach and the comparisons with other two approaches are given, which demonstrate that the modeling and control method presented in this paper are very effective and superior in controlling the fast-responding, strongly nonlinear and open-loop unstable system. This paper gives the real experimental evidence that the RBF-ARX model is capable of not only globally, but also locally capturing and quantifying a nonlinear and fast-response system's behavior, and the model-based predictive control strategy is able to work quite well in a wide working-range of the nonlinear system.",
keywords = "Magnetic levitation system, Parameter optimization, Predictive control, RBF-ARX model, Real-time control",
author = "Yemei Qin and Hui Peng and Wenjie Ruan and Jun Wu and Jiacheng Gao",
year = "2014",
month = jan,
day = "1",
doi = "10.1016/j.jprocont.2013.10.016",
language = "English",
volume = "24",
pages = "93--112",
journal = "Journal of Process Control",
issn = "0959-1524",
publisher = "Elsevier Limited",
number = "1",

}

RIS

TY - JOUR

T1 - A modeling and control approach to magnetic levitation system based on state-dependent ARX model

AU - Qin, Yemei

AU - Peng, Hui

AU - Ruan, Wenjie

AU - Wu, Jun

AU - Gao, Jiacheng

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Magnetic levitation (Maglev) systems are usually strongly nonlinear, open-loop unstable and fast responding. In order to control the position of the steel ball in a Maglev system, a data-driven modeling approach and control strategy is presented in this paper. A state-dependent AutoRegressive with eXogenous input (SD-ARX) model is built to represent the dynamic behavior between the current of electromagnetic coil and the position of the ball. State-dependent functional coefficients of the SD-ARX model are approximated by Gaussian radial basis function (RBF) neural networks. The model parameters are identified offline by applying the structured nonlinear parameter optimization method (SNPOM). Based on the model, a predictive controller is designed to stabilize the magnetic levitation ball to a given position or to make it track a desired trajectory. The real-time control results of the proposed approach and the comparisons with other two approaches are given, which demonstrate that the modeling and control method presented in this paper are very effective and superior in controlling the fast-responding, strongly nonlinear and open-loop unstable system. This paper gives the real experimental evidence that the RBF-ARX model is capable of not only globally, but also locally capturing and quantifying a nonlinear and fast-response system's behavior, and the model-based predictive control strategy is able to work quite well in a wide working-range of the nonlinear system.

AB - Magnetic levitation (Maglev) systems are usually strongly nonlinear, open-loop unstable and fast responding. In order to control the position of the steel ball in a Maglev system, a data-driven modeling approach and control strategy is presented in this paper. A state-dependent AutoRegressive with eXogenous input (SD-ARX) model is built to represent the dynamic behavior between the current of electromagnetic coil and the position of the ball. State-dependent functional coefficients of the SD-ARX model are approximated by Gaussian radial basis function (RBF) neural networks. The model parameters are identified offline by applying the structured nonlinear parameter optimization method (SNPOM). Based on the model, a predictive controller is designed to stabilize the magnetic levitation ball to a given position or to make it track a desired trajectory. The real-time control results of the proposed approach and the comparisons with other two approaches are given, which demonstrate that the modeling and control method presented in this paper are very effective and superior in controlling the fast-responding, strongly nonlinear and open-loop unstable system. This paper gives the real experimental evidence that the RBF-ARX model is capable of not only globally, but also locally capturing and quantifying a nonlinear and fast-response system's behavior, and the model-based predictive control strategy is able to work quite well in a wide working-range of the nonlinear system.

KW - Magnetic levitation system

KW - Parameter optimization

KW - Predictive control

KW - RBF-ARX model

KW - Real-time control

U2 - 10.1016/j.jprocont.2013.10.016

DO - 10.1016/j.jprocont.2013.10.016

M3 - Journal article

AN - SCOPUS:84890544515

VL - 24

SP - 93

EP - 112

JO - Journal of Process Control

JF - Journal of Process Control

SN - 0959-1524

IS - 1

ER -