Investigation of the Transverse Flux Linear Synchronous Motor Integrated With Propulsion, Levitation and Guidance for the Maglev Train

Lv, Gang; Cui, Leilei; Zhi, Ruodong; Zhou, Tong; Liu, Yaqing

doi:10.1109/tte.2023.3246070

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…The tractive force of the maglev train is provided by the long stator synchronous linear motor; ψ d , ψ q , i d , i q are the magnetic linkage and current components of the stator winding on the d-q axis, respectively; and τ is the stator pole distance. According to the rotor magnetic field-oriented control strategy, i d is usually controlled to 0, and the model is described as follows [11,38]:…”

Section: Tractive Force Modelmentioning

confidence: 99%

“…The conventional braking force is created by the long stator linear motor, that is, the tractive force is reversed. The emergency braking force is generated by the eddy current effect between the lateral braking magnetic pole and the guideway and is expressed by [11,39]…”

Section: Braking Force Modelmentioning

confidence: 99%

“…However, during the train running process, the location and speed states frequently change, and the acceleration and deceleration actions are contained in the continuous domain, which may result in enormous state space and dimension errors [8]. Thus, speed curve tracking involves vital steps to ensure Energies 2023, 16, 4566 2 of 15 that the optimal speed curve is energy-saving and provides anti-disturbance tracking control with high precision [9][10][11]. High-performance operation control algorithms can dynamically adjust actual operation curves based on line parameters, reduce the adverse impact of operation resistance, precisely track the preset speed profile, and improve train control quality.…”

Section: Introductionmentioning

confidence: 99%

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Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network

Zhang

Lin

et al. 2023

Energies

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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.

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Section: Tractive Force Modelmentioning

confidence: 99%

Section: Braking Force Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network

Zhang

Lin

et al. 2023

Energies

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“…Magnetic levitation, as an advanced technology for rail transportation, will promote further development and application in transportation [2,3]. At present, China, the US, Germany and Japan are the four most developed countries in maglev technology, and are clearly leading in the international arena with abundant research results [4][5][6]. Meanwhile, countries such as Switzerland, Canada, and South Korea are actively researching new maglev rail technology [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Xu,

Fan,

et al. 2023

PLoS ONE

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Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

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Electromagnetic characteristic analysis and design of a linear motor used for ultra-high-speed EMS maglev train

Gao,

Shi,

Gao

et al. 2024

Sci. China Technol. Sci.

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Investigation of the Transverse Flux Linear Synchronous Motor Integrated With Propulsion, Levitation and Guidance for the Maglev Train

Cited by 6 publications

References 17 publications

Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network

Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Electromagnetic characteristic analysis and design of a linear motor used for ultra-high-speed EMS maglev train

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