WeChat Mini Program
Old Version Features

    • Finite-time Control of High-speed Maglev Train Levitation System Based on Terminal Sliding Mode

    Yifan Luo,Fei Ni,Guobin Lin,Junqi Xu

    2024 4th International Conference on Control Theory and Applications (ICoCTA)（2024）

    Institute of Rail Transit

    Cited 0|Views1
    Abstract
    The levitation system of the high-speed maglev train is a complex system that must quickly adjust after disturbances to minimize their impact on the train's movements. This paper presents a type of non-singular terminal sliding mode (NTSM) finite-time control approach for the levitation gap tracking of the high-speed maglev train. First, we analyze the levitation system's dynamic characteristics with unknown disturbances. Secondly, a nonlinear observer is used to estimate disturbances in the system. Then, based on the estimated disturbance and considering the potential singularity of the sliding mode surface, a non-singular terminal sliding mode control method (NTSMC) is devised with an improved power type reaching rate. Finally, the closed-loop system is proven to be stable. The simulation results show that under disturbance conditions the proposed method can significantly reduce the convergence time and diminish the levitation gap fluctuations by more than 90%. This offers a new way to make Maglev trains more reliable and stable at high speeds.
    More
    Translated text
    Key words
    high-speed maglev train,levitation system,disturbance observer,non-singular terminal sliding mode control,finite time control
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined