IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i6p1451-d150611.html
   My bibliography  Save this article

Design of a Path-Tracking Steering Controller for Autonomous Vehicles

Author

Listed:
  • Chuanyang Sun

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xin Zhang

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Lihe Xi

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Ying Tian

    (Beijing Jiaotong University Yangtze River Delta Research Institute, Zhenjiang 212009, China)

Abstract

This paper presents a linearization method for the vehicle and tire models under the model predictive control (MPC) scheme, and proposes a linear model-based MPC path-tracking steering controller for autonomous vehicles. The steering controller is designed to minimize lateral path-tracking deviation at high speeds. The vehicle model is linearized by a sequence of supposed steering angles, which are obtained by assuming the vehicle can reach the desired path at the end of the MPC prediction horizon and stay in a steady-state condition. The lateral force of the front tire is directly used as the control input of the model, and the rear tire’s lateral force is linearized by an equivalent cornering stiffness. The course-direction deviation, which is the angle between the velocity vector and the path heading, is chosen as a control reference state. The linearization model is validated through the simulation, and the results show high prediction accuracy even in regions of large steering angle. This steering controller is tested through simulations on the CarSim-Simulink platform (R2013b, MathWorks, Natick, MA, USA), showing the improved performance of the present controller at high speeds.

Suggested Citation

  • Chuanyang Sun & Xin Zhang & Lihe Xi & Ying Tian, 2018. "Design of a Path-Tracking Steering Controller for Autonomous Vehicles," Energies, MDPI, vol. 11(6), pages 1-17, June.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1451-:d:150611
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/6/1451/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/6/1451/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aijuan Li & Wanzhong Zhao & Xibo Wang & Xuyun Qiu, 2018. "ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sara Abdallaoui & El-Hassane Aglzim & Ahmed Chaibet & Ali Kribèche, 2022. "Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques," Energies, MDPI, vol. 15(4), pages 1-19, February.
    2. Jie Tian & Jie Ding & Yongpeng Tai & Ning Chen, 2018. "Hierarchical Control of Nonlinear Active Four-Wheel-Steering Vehicles," Energies, MDPI, vol. 11(11), pages 1-14, October.
    3. Leon Prochowski & Mateusz Ziubiński & Patryk Szwajkowski & Mirosław Gidlewski & Tomasz Pusty & Tomasz Lech Stańczyk, 2021. "Impact of Control System Model Parameters on the Obstacle Avoidance by an Autonomous Car-Trailer Unit: Research Results," Energies, MDPI, vol. 14(10), pages 1-31, May.
    4. Hazel Si Min Lim & Araz Taeihagh, 2019. "Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities," Sustainability, MDPI, vol. 11(20), pages 1-28, October.
    5. Jie Tian & Jun Tong & Shi Luo, 2018. "Differential Steering Control of Four-Wheel Independent-Drive Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-18, October.
    6. Francesco Calise & Mário Costa & Qiuwang Wang & Xiliang Zhang & Neven Duić, 2018. "Recent Advances in the Analysis of Sustainable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-30, September.

    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. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.

    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:11:y:2018:i:6:p:1451-:d:150611. 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.