A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

Zhao, Yuanzhe; Peng, Fei; Ren, Linjie; Lin, Guobin; Xu, Junqi

doi:10.1109/access.2020.3025968

Cited by 3 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-speed maglev trains can reach over 300-600 km/h, whereas medium-low-speed maglev trains can only reach below 300 km/h. Different from wheel-rail transit systems, maglev trains are affected by various resistance factors such as slope, air resistance, the eddy current effect, and back electromotive force during operation [3][4][5]. In the operation control field of maglev transportation, it has become more and more difficult in recent years to reduce the impact of train running resistance, improve train speed tracking accuracy, and design high-performance train running controllers.…”

Section: Introductionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, the accuracy of the model was veri ed by experimental data. In order to further explore the change process of perception structure in maglev train, nonlinear random matrix was adopted to modify the deep learning model [8]. us, an optimization model that can re ect the changes of magnetic levitation perception structure can be obtained, through which the targeted analysis of the perception structure can be carried out.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model for Stock Excess Return Prediction Based on Nonlinear Random Matrix and Esg Factor

Meng

Yahya

Chai

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Aiming at the problem that the traditional model has low accuracy in describing stock excess return, in order to further analyze the change law of stock excess, based on the nonlinear random matrix and esg factor theory, the traditional learning model is analyzed, and the corresponding optimized deep learning model is obtained by introducing the single ring theorem and statistical data. Through the analysis and research of related indexes, the change rules of different indexes are obtained, and the optimization model is used to calculate and forecast the excess return of stocks. The results show that the statistics and spectral radius show typical local linear variation with the increase of eigenvalue. The corresponding statistics show a trend of gradual increase. The corresponding spectral radius has a decreasing variation law, and the two curves have obvious symmetry at some eigenvalues. It can be seen from the change curves under different factors that the change trend of the yield curve is mainly affected by the investment factor, while the change rule of the specific value of the yield curve is controlled by the profit factor. This shows that the two factors have the same influence on the stock excess return. The influence of optimized deep learning model on stock excess index has typical linear characteristics, which can be divided into linear increase and linear decline according to different change rules. The basic type has the greatest influence, while the corresponding pattern analysis type has the least influence. Finally, the method of experimental verification is used to verify the stock excess data, and the results show that the optimized deep learning model can better characterize the experimental results. Therefore, the optimized deep learning model based on nonlinear random matrix and esg factor can carry out targeted analysis of different types of stock returns, thus improving research ideas and calculation methods for the application of deep learning model in different fields.

show abstract

Review of Fault-Tolerant Control Methods for Suspension Systems: From Road Vehicles to Maglev Trains

Ni,

Luo,

et al. 2024

Mathematics

View full text Add to dashboard Cite

Road vehicles and maglev trains have garnered significant attention, with their suspension systems being crucial for safe and stable performance. However, these systems can be compromised by faults such as sensor and actuator failures, posing risks to stability and safety. This review explores fault-tolerant controls for suspension systems, driven by the need to enhance fault tolerance in such scenarios. We examine the dynamic similarities between the semi-active/active suspension systems in road vehicles and the suspension systems in maglev trains, offering a comprehensive summary of fault-tolerant control strategies for both. Our analysis covers the histories, technical characteristics, fundamentals, modeling, mathematical derivations, and control objectives of both systems. The review categorizes fault-tolerant control methods into hardware redundancy, passive fault-tolerant control, and active fault-tolerant control. We evaluate the advantages and disadvantages of these strategies and propose future directions for the development of fault-tolerant control in suspension systems.

show abstract

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

Cited by 3 publications

References 35 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

Deep Learning Model for Stock Excess Return Prediction Based on Nonlinear Random Matrix and Esg Factor

Review of Fault-Tolerant Control Methods for Suspension Systems: From Road Vehicles to Maglev Trains

Contact Info

Product

Resources

About