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On Extended State Sequence Prediction Based MPC Path Tracking Control for Autonomous Vehicle
ID：74 View Protection：ATTENDEE Updated Time：2025-05-06 15:04:28 Hits：952 Oral

Start Time：Pending（Asia/Shanghai）

Duration：Pending

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Abstract
路径跟踪控制是自动驾驶汽车的一项关键技术，但由于不同的路况和模型不确定性引起的动态干扰，它面临着重大挑战。为了解决这些问题，本文提出了一种用于车辆路径跟踪的扩展状态基于序列的模型预测控制器 （PESO-MPC）。首先，开发了扩展状态观测器（ESO）来实时估计动态扰动，并与扰动预测模型的在线识别相结合，得到扰动序列。随后，建立了一个增强的 MPC 框架，将扩展状态序列纳入预测模型。通过求解二次规划问题，推导了具有动态抗扰能力的控制律。仿真和实车实验结果表明，PESO-MPC 具有优异的性能，与传统 MPC 和 NMPC 相比，均方根误差 （RMSE） 分别降低了 33.88% 和 18.64% 以上。
Keywords
extended state observer (ESO),model predictive control (MPC),path tracking control,autonomous vehicle
Speaker
Guochen Liu
PhD Student Tianjin University

Submission Author
Guochen Liu Tianjin University
Wenhao Xiao Tianjin University
Guojie Tang Academy of mathematics and Systems Sciences, Chinese Academy of Sciences
Kang Song Tianjin University
Wenchao Xue Academy of mathematics and Systems Sciences, Chinese Academy of Sciences
Hui Xie Tianjin University
Tielong Shen Dalian University of Technology
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Important Date

  • Conference Date

    Jun 05

    2025

    to

    Jun 01

    2026

  • May 30 2025

    Draft paper submission deadline

  • Jun 08 2025

    Registration deadline

Sponsored By
China Southeast University
IEEE Power Electronics Society
Jiangsu Association of Automation
Nanjing Section IE Chapter
Organized By
Southeast University
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