Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network
The speed profile tracking calculation of high-speed maglev trains is mainly affected by running resistance. In order to reduce the adverse effects and improve tracking accuracy, this paper presents a maglev train operation control method based on a fractional-order sliding mode adaptive and diagona...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/12/4566 |
| _version_ | 1797595140273995776 |
|---|---|
| author | Wenbai Zhang Guobin Lin Keting Hu Zhiming Liao Huan Wang |
| author_facet | Wenbai Zhang Guobin Lin Keting Hu Zhiming Liao Huan Wang |
| author_sort | Wenbai Zhang |
| collection | DOAJ |
| description | The speed profile tracking calculation of high-speed maglev trains is mainly affected by running resistance. In order to reduce the adverse effects and improve tracking accuracy, this paper presents a maglev train operation control method based on a fractional-order sliding mode adaptive and diagonal recurrent neural network (FSMA-DRNN). First, the kinematic resistance equation is established due to the three types of resistance that occur during the actual operation of a train: air resistance, guide eddy current resistance, and suspension frame generator coil resistance. Then, the FSMA-DRNN control law and parameter update law are designed, and a FSMA-DRNN operation controller is composed of three parts: speed feed forward, fractional-order sliding mode adaptive equivalent control, and diagonal recurrent neural network resistance compensation. Furthermore, by using the designed operation controller, it is proven effective by the Lyapunov theory for the stability of the closed-loop control system. Apart from the proposed theoretical analysis, the proposed approaches are verified by experiments on the high-speed maglev hardware-in-the-loop simulation platform Rt-Lab, in line with the 29.86 km test line and a five-car train from the Shanghai maglev, showing the effectiveness and superiority for operation optimization. |
| first_indexed | 2024-03-11T02:32:14Z |
| format | Article |
| id | doaj.art-f17b6fce169f48d0a784ef493372a8b1 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T02:32:14Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f17b6fce169f48d0a784ef493372a8b12023-11-18T10:11:08ZengMDPI AGEnergies1996-10732023-06-011612456610.3390/en16124566Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural NetworkWenbai Zhang0Guobin Lin1Keting Hu2Zhiming Liao3Huan Wang4Institute of Rail Transit, Tongji University, Shanghai 201804, ChinaNational Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, ChinaInstitute of Rail Transit, Tongji University, Shanghai 201804, ChinaNational Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, ChinaNational Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, ChinaThe speed profile tracking calculation of high-speed maglev trains is mainly affected by running resistance. In order to reduce the adverse effects and improve tracking accuracy, this paper presents a maglev train operation control method based on a fractional-order sliding mode adaptive and diagonal recurrent neural network (FSMA-DRNN). First, the kinematic resistance equation is established due to the three types of resistance that occur during the actual operation of a train: air resistance, guide eddy current resistance, and suspension frame generator coil resistance. Then, the FSMA-DRNN control law and parameter update law are designed, and a FSMA-DRNN operation controller is composed of three parts: speed feed forward, fractional-order sliding mode adaptive equivalent control, and diagonal recurrent neural network resistance compensation. Furthermore, by using the designed operation controller, it is proven effective by the Lyapunov theory for the stability of the closed-loop control system. Apart from the proposed theoretical analysis, the proposed approaches are verified by experiments on the high-speed maglev hardware-in-the-loop simulation platform Rt-Lab, in line with the 29.86 km test line and a five-car train from the Shanghai maglev, showing the effectiveness and superiority for operation optimization.https://www.mdpi.com/1996-1073/16/12/4566high-speed maglevspeed trackingrunning resistancefractional orderdiagonal recurrent neural networks |
| spellingShingle | Wenbai Zhang Guobin Lin Keting Hu Zhiming Liao Huan Wang Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network Energies high-speed maglev speed tracking running resistance fractional order diagonal recurrent neural networks |
| title | Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network |
| title_full | Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network |
| title_fullStr | Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network |
| title_full_unstemmed | Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network |
| title_short | Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network |
| title_sort | operation control method for high speed maglev based on fractional order sliding mode adaptive and diagonal recurrent neural network |
| topic | high-speed maglev speed tracking running resistance fractional order diagonal recurrent neural networks |
| url | https://www.mdpi.com/1996-1073/16/12/4566 |
| work_keys_str_mv | AT wenbaizhang operationcontrolmethodforhighspeedmaglevbasedonfractionalorderslidingmodeadaptiveanddiagonalrecurrentneuralnetwork AT guobinlin operationcontrolmethodforhighspeedmaglevbasedonfractionalorderslidingmodeadaptiveanddiagonalrecurrentneuralnetwork AT ketinghu operationcontrolmethodforhighspeedmaglevbasedonfractionalorderslidingmodeadaptiveanddiagonalrecurrentneuralnetwork AT zhimingliao operationcontrolmethodforhighspeedmaglevbasedonfractionalorderslidingmodeadaptiveanddiagonalrecurrentneuralnetwork AT huanwang operationcontrolmethodforhighspeedmaglevbasedonfractionalorderslidingmodeadaptiveanddiagonalrecurrentneuralnetwork |