IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p4020-d781929.html
   My bibliography  Save this article

ELM-Based Non-Singular Fast Terminal Sliding Mode Control Strategy for Vehicle Platoon

Author

Listed:
  • Chengmei Wang

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Yuchuan Du

    (The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China
    College of Transportation Engineering, Tongji University, Shanghai 201804, China)

Abstract

Vehicle platoon is one of the innovations in the automated highway systems, which has the potential to reduce fuel consumption, alleviate traffic congestion and lighten the driver’s burden. How to control the vehicle effectively to ensure the stability of the queue is a challenge. Aiming to overcome the shortcomings of the platoon control method based on traditional sliding mode control, a non-singular terminal sliding mode control method optimized by the extreme learning machine is proposed in this paper. Firstly, the vehicle longitudinal dynamics are derived from the analysis of the forces acting on the vehicle in the longitudinal direction. A constant time headway policy is taken as the spacing policy. The modified non-singular terminal sliding mode control method has outstanding performance, simulation results demonstrate that the following vehicles can rapidly track the trajectory of the leading vehicle in the platoon with less spacing error and guarantee string stability. In this study, several experiments are set up to consider the disturbance and other uncertain practical factors. The performance of the proposed method is superior to the traditional sliding mode control method. Experimental results show that the proposed method can significantly reduce chattering and has good robustness under the circumstances of the disturbance.

Suggested Citation

  • Chengmei Wang & Yuchuan Du, 2022. "ELM-Based Non-Singular Fast Terminal Sliding Mode Control Strategy for Vehicle Platoon," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4020-:d:781929
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/4020/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/4020/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    2. Saeed Vasebi & Yeganeh M. Hayeri, 2021. "Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    3. Run Mao & Hongli Gao & Liang Guo, 2020. "A Novel Collision-Free Navigation Approach for Multiple Nonholonomic Robots Based on ORCA and Linear MPC," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K., 2024. "A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).
    2. Hang Shen & Lin Li & Haihong Zhu & Yu Liu & Zhenwei Luo, 2021. "Exploring a Pricing Model for Urban Rental Houses from a Geographical Perspective," Land, MDPI, vol. 11(1), pages 1-28, December.
    3. Han, Yu & Zhang, Mingyu & Guo, Yanyong & Zhang, Le, 2022. "A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    4. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    5. Zhang, Kunpeng & Feng, Xiaoliang & Jia, Ning & Zhao, Liang & He, Zhengbing, 2022. "TSR-GAN: Generative Adversarial Networks for Traffic State Reconstruction with Time Space Diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    6. Liu, Yang & Song, Yaolun & Zhang, Yan & Liao, Zhifang, 2022. "WT-2DCNN: A convolutional neural network traffic flow prediction model based on wavelet reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    7. Wu, Jiaxin & Zhou, Xubing & Peng, Yi & Zhao, Xiaojun, 2022. "Recurrence analysis of urban traffic congestion index on multi-scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    8. Wang, Yun & Xu, Houhua & Song, Mengmeng & Zhang, Fan & Li, Yifen & Zhou, Shengchao & Zhang, Lingjun, 2023. "A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting," Applied Energy, Elsevier, vol. 333(C).
    9. Hu, Xu & Li, Dongshuang & Yu, Zhaoyuan & Yan, Zhenjun & Luo, Wen & Yuan, Linwang, 2022. "Quantum harmonic oscillator model for fine-grained expressway traffic volume simulation considering individual heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    10. Ziwen Song & Feng Sun & Rongji Zhang & Yingcui Du & Guiliang Zhou, 2021. "An Improved Cellular Automaton Traffic Model Based on STCA Model Considering Variable Direction Lanes in I-VICS," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    11. Wang, Kun & Xiong, Li & Xue, Rudan, 2024. "Real-time data stream learning for emergency decision-making under uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    12. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    13. Krešimir Kušić & Edouard Ivanjko & Filip Vrbanić & Martin Gregurić & Ivana Dusparic, 2021. "Spatial-Temporal Traffic Flow Control on Motorways Using Distributed Multi-Agent Reinforcement Learning," Mathematics, MDPI, vol. 9(23), pages 1-28, November.
    14. Salvatore Antonio Biancardo & Francesco Avella & Ernesto Di Lisa & Xinqiang Chen & Francesco Abbondati & Gianluca Dell’Acqua, 2021. "Multiobjective Railway Alignment Optimization Using Ballastless Track and Reduced Cross-Section in Tunnel," Sustainability, MDPI, vol. 13(19), pages 1-19, September.
    15. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K. & Cao, Shuhan, 2023. "A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    16. Philip Cammin & Jingjing Yu & Stefan Voß, 2023. "Tiered prediction models for port vessel emissions inventories," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 142-169, March.
    17. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    18. Dong, Hanxuan & Ding, Fan & Tan, Huachun & Zhang, Hailong, 2022. "Laplacian integration of graph convolutional network with tensor completion for traffic prediction with missing data in inter-city highway network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    19. Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    20. Li, Yisha & Chen, Guoxi & Zhang, Ya, 2023. "Cycle-based signal timing with traffic flow prediction for dynamic environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:4020-:d:781929. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.