Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control
To improve the stability of the maglev train’s levitation and enhance its dynamic performance during high-speed operation, this paper establishes a control strategy based on backstepping control theory and fuzzy neural network. Firstly, this paper presents a vehicle/guideway numerical model based on...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-03-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251330405 |
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| _version_ | 1850275453927948288 |
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| author | Hao Zeng Jingyu Huang Ziyang Zhang Qifei Lu |
| author_facet | Hao Zeng Jingyu Huang Ziyang Zhang Qifei Lu |
| author_sort | Hao Zeng |
| collection | DOAJ |
| description | To improve the stability of the maglev train’s levitation and enhance its dynamic performance during high-speed operation, this paper establishes a control strategy based on backstepping control theory and fuzzy neural network. Firstly, this paper presents a vehicle/guideway numerical model based on the Shanghai Maglev line (SML) structure, considering the guideway’s flexible deformation and vertical irregularity. The coupled vibration of the vehicle and guideway is solved using an implicit time-domain analysis method. Then, a backstepping control (BSC) scheme is designed using multi-body dynamics and electromagnetic force equations. To improve its control performance, a fuzzy neural network control strategy (BFNNC) is designed to mimic the BSC scheme to achieve the suspension control of high-speed maglev trains. Finally, the paper confirms the reliability of the numerical model with the measured data of the SML. The dynamic responses of the vehicle and guideway are studied systematically under two different control schemes at high speeds. The control performance of the BFNNC strategy and the BSC scheme is compared considering different train speeds and disturbance force. The conclusion shows that compared to the BSC scheme, the BFNNC strategy has a superior control performance and robustness performance. |
| format | Article |
| id | doaj-art-4345390d10014959a49d90df2ca7e00a |
| institution | OA Journals |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-4345390d10014959a49d90df2ca7e00a2025-08-20T01:50:44ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-03-011710.1177/16878132251330405Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network controlHao Zeng0Jingyu Huang1Ziyang Zhang2Qifei Lu3College of Civil Engineering, Tongji University, Shanghai, ChinaNational Maglev Transportation Engineering R&D Center, Tongji University, Shanghai, ChinaCollege of Civil Engineering, Tongji University, Shanghai, ChinaCollege of Civil Engineering, Tongji University, Shanghai, ChinaTo improve the stability of the maglev train’s levitation and enhance its dynamic performance during high-speed operation, this paper establishes a control strategy based on backstepping control theory and fuzzy neural network. Firstly, this paper presents a vehicle/guideway numerical model based on the Shanghai Maglev line (SML) structure, considering the guideway’s flexible deformation and vertical irregularity. The coupled vibration of the vehicle and guideway is solved using an implicit time-domain analysis method. Then, a backstepping control (BSC) scheme is designed using multi-body dynamics and electromagnetic force equations. To improve its control performance, a fuzzy neural network control strategy (BFNNC) is designed to mimic the BSC scheme to achieve the suspension control of high-speed maglev trains. Finally, the paper confirms the reliability of the numerical model with the measured data of the SML. The dynamic responses of the vehicle and guideway are studied systematically under two different control schemes at high speeds. The control performance of the BFNNC strategy and the BSC scheme is compared considering different train speeds and disturbance force. The conclusion shows that compared to the BSC scheme, the BFNNC strategy has a superior control performance and robustness performance.https://doi.org/10.1177/16878132251330405 |
| spellingShingle | Hao Zeng Jingyu Huang Ziyang Zhang Qifei Lu Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control Advances in Mechanical Engineering |
| title | Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control |
| title_full | Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control |
| title_fullStr | Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control |
| title_full_unstemmed | Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control |
| title_short | Nonlinear dynamics analysis of the high-speed maglev vehicle/guideway coupled system with backstepping fuzzy-neural-network control |
| title_sort | nonlinear dynamics analysis of the high speed maglev vehicle guideway coupled system with backstepping fuzzy neural network control |
| url | https://doi.org/10.1177/16878132251330405 |
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