IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2305-d240394.html
   My bibliography  Save this article

Nonlinear Synchronous Control for H-Type Gantry Stage Used in Electric Vehicles Manufacturing

Author

Listed:
  • Ran Chen

    (School of Energy and Power Engineering, Beihang University, Beijing 100191, China)

  • Zongxia Jiao

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    Ningbo Institute of Technology, Beihang University, Ningbo 315800, China)

  • Liang Yan

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    Ningbo Institute of Technology, Beihang University, Ningbo 315800, China)

  • Yaoxing Shang

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
    Ningbo Institute of Technology, Beihang University, Ningbo 315800, China)

  • Shuai Wu

    (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China)

Abstract

The H-type gantry stage (HGS) is widely used in electric vehicle manufacturing and other fields. However, resulting from the existence of mechanical coupling, the synchronous control problem of HGS always troubles many engineers. Most synchronization schemes were either engaged in improving each motor’s tracking performance or committed to pure motion synchronization only. However, tracking and synchronous performance are interconnected, because of the mechanical coupling. In this paper, a rigid assumed system model of HGS, concerning the effects of mid-beam rotary inertia, mid-beam stiffness, and end-effector movement, is presented. Based on the proposed model, an adaptive robust synchronous control based on a rigid assumed model (ARSCR) is proposed to improve both synchronous and tracking performance of the HGS. From the Lyapunov analysis, the proposed ARSCR can achieve the convergence of synchronous error and tracking error, simultaneously. An HGS driven by dual linear motors is built and used to perform the experimental verification. The experimental results indicate the effectiveness of the proposed method.

Suggested Citation

  • Ran Chen & Zongxia Jiao & Liang Yan & Yaoxing Shang & Shuai Wu, 2019. "Nonlinear Synchronous Control for H-Type Gantry Stage Used in Electric Vehicles Manufacturing," Energies, MDPI, vol. 12(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2305-:d:240394
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2305/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/12/2305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongwen He & Jiankun Peng & Rui Xiong & Hao Fan, 2014. "An Acceleration Slip Regulation Strategy for Four-Wheel Drive Electric Vehicles Based on Sliding Mode Control," Energies, MDPI, vol. 7(6), pages 1-16, June.
    2. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    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. Xu Lei & Xi Zhao & Guiping Wang & Weiyu Liu, 2019. "A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power," Energies, MDPI, vol. 12(19), pages 1-24, September.
    2. Jiangyi Lv & Hongwen He & Wei Liu & Yong Chen & Fengchun Sun, 2019. "Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales," Energies, MDPI, vol. 12(7), pages 1-24, April.
    3. Liu, Guoan & Xu, Cheng & Li, Haomiao & Jiang, Kai & Wang, Kangli, 2019. "State of charge and online model parameters co-estimation for liquid metal batteries," Applied Energy, Elsevier, vol. 250(C), pages 677-684.
    4. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    5. Kanghyun Nam & Yoichi Hori & Choonyoung Lee, 2015. "Wheel Slip Control for Improving Traction-Ability and Energy Efficiency of a Personal Electric Vehicle," Energies, MDPI, vol. 8(7), pages 1-21, July.
    6. Lingfei Wu & Jinfang Gou & Lifang Wang & Junzhi Zhang, 2015. "Acceleration Slip Regulation Strategy for Distributed Drive Electric Vehicles with Independent Front Axle Drive Motors," Energies, MDPI, vol. 8(5), pages 1-30, May.
    7. Cheng Lin & Zhifeng Xu, 2015. "Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization," Energies, MDPI, vol. 8(5), pages 1-17, April.
    8. Xudong Zhang & Dietmar Göhlich, 2017. "A hierarchical estimator development for estimation of tire-road friction coefficient," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    9. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    10. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    11. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    12. Yin Hua & Min Xu & Mian Li & Chengbin Ma & Chen Zhao, 2015. "Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles," Energies, MDPI, vol. 8(5), pages 1-22, April.
    13. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    14. Binh-Minh Nguyen & Hung Van Nguyen & Minh Ta-Cao & Michihiro Kawanishi, 2020. "Longitudinal Modelling and Control of In-Wheel-Motor Electric Vehicles as Multi-Agent Systems," Energies, MDPI, vol. 13(20), pages 1-28, October.
    15. Hong Zhang & Li Zhao & Yong Chen, 2015. "A Lossy Counting-Based State of Charge Estimation Method and Its Application to Electric Vehicles," Energies, MDPI, vol. 8(12), pages 1-18, December.
    16. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2016. "A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty," Energy, Elsevier, vol. 109(C), pages 933-946.
    17. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    18. Thanh Vo-Duy & Minh C. Ta & Bảo-Huy Nguyễn & João Pedro F. Trovão, 2020. "Experimental Platform for Evaluation of On-Board Real-Time Motion Controllers for Electric Vehicles," Energies, MDPI, vol. 13(23), pages 1-28, December.
    19. Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    20. Xudong Zhang & Dietmar Göhlich, 2017. "Integrated Traction Control Strategy for Distributed Drive Electric Vehicles with Improvement of Economy and Longitudinal Driving Stability," Energies, MDPI, vol. 10(1), pages 1-18, January.

    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:jeners:v:12:y:2019:i:12:p:2305-:d:240394. 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.