考虑扰动主动补偿的自动驾驶铰接列车轨迹跟踪控制

doi:10.3969/j.issn.1674-8484.2026.05.010

摘要/Abstract

摘要：

为提高自动驾驶铰接列车在外部多源干扰与内部模型失配下的轨迹跟踪精度与稳定性，提出一种融合扰动主动补偿与模型预测控制的轨迹跟踪控制方法（Ro-ADRC）。通过建立牵引车-半挂车动力学模型及轨迹跟踪偏差方程，设计自抗扰与模型预测融合控制架构，利用扩张状态观测器实时估计补偿内外部多源总扰动，并在物理约束下滚动优化求解最优控制序列。仿真结果表明：在双移线工况下，Ro-ADRC 相较于主动抗扰控制（ADRC）、模型预测控制（MPC）、管道模型预测控制（Tube-MPC）与非线性模型预测控制（NMPC）等方法牵引车最大横向误差降低了 96.7%、83.5%、69.6% 和 69.5%；在Z型轨迹下，牵引车最大横向误差降低了 83.6%、69.2%、21.3% 和 43.4%；硬件在环试验也进一步验证了 Ro-ADRC 的鲁棒性。该方法能够显著提升系统抗扰动性能，可为复杂工况下铰接列车高精度稳定控制提供可靠技术支撑。

关键词: 自动驾驶铰接列车, 轨迹跟踪, 模型预测控制, 扰动主动补偿, 滚动时域优化

Abstract:

A trajectory tracking method of rolling optimization based ADRC (Ro-ADRC) integrating active disturbance compensation with model predictive control was proposed to enhance the trajectory tracking accuracy and stability of autonomous articulated vehicles subjected to external multi-source disturbances and internal model mismatches. A fusion control architecture of active disturbance rejection and model predictive control was designed by establishing the dynamic model of the tractor-semitrailer and the trajectory tracking deviation equation. An extended state observer was used to estimate and compensate for the total internal and external multi-source disturbances in real time, and the optimal control sequence was solved through receding horizon optimization under physical constraints. Simulation results show that Ro-ADRC reduces the tractor's maximum lateral error under double-lane-change conditions by 96.7%, 83.5%, 69.6%, and 69.5% compared to active disturbance rejection control (ADRC), model predictive control (MPC), tube-based MPC (Tube-MPC), and nonlinear model predictive control (NMPC), respectively; under Z-shaped trajectories, the tractor's maximum lateral error is reduced by 83.6%, 69.2%, 21.3%, and 43.4%. Hardware-in-the-loop tests also further verify the robustness of Ro-ADRC. The proposed method can significantly enhance the anti-disturbance performance of the system and provide reliable technical support for the high-precision stable control of articulated vehicles under complex working conditions.

Key words: autonomous articulated vehicle, trajectory tracking, model predictive control (MPC), active disturbance compensation, rolling horizon optimization

中图分类号:

U471.1

廖亚萍, 云宇豪, 金立生, 谢宪毅, 周彬, 王章宇. 考虑扰动主动补偿的自动驾驶铰接列车轨迹跟踪控制[J]. 汽车安全与节能学报, 2026, 17(5): 609-620.

LIAO Yaping, YUN Yuhao, JIN Lisheng, XIE Xianyi, ZHOU Bin, WANG Zhangyu. Trajectory tracking control of autonomous articulated vehicles with active disturbance compensation[J]. Journal of Automotive Safety and Energy, 2026, 17(5): 609-620.

图/表 8

图1 车辆三自由度动力学模型

图2 车辆轨迹跟踪模型

图3 控制系统整体结构框架

图4 双移线工况下不同控制算法的轨迹跟踪对比结果

图5 轨迹跟踪结果分析

图6 Z型轨迹下不同控制算法的轨迹跟踪对比结果

图7 轨迹跟踪结果分析

图8 2种工况下不同控制算法的轨迹跟踪对比结果

参考文献 19

[1]	YU Huilong, LI Erhang, Corno M, et al. Bi-level path tracking control of tractor semi-trailers by coordinated active front steering and differential braking[J]. Proc Insti Mech Engi, Part D: J Autom Engi, 2025, 239(8): 3667-3681.
[2]	范颂华, 么新鹏, 李一鸣, 等. 自动驾驶技术赋能干线物流:现状、挑战与未来发展路径[J]. 交通运输研究, 2025, 11(4): 113-121. doi: 10.16503/j.cnki.2095-9931.2025.04.010
	FAN Songhua, YAO Xinpeng, LI Yiming, et al. Autono-mous driving technology empowering trunk logistics: current status, challenges, and future development paths[J]. Transport Res, 2025, 11(4): 113-121. (in Chinese).
[3]	WANG Ziheng, CHEN Long, CAI Yingfeng, et al. Adaptive coordinated control strategy for autonomous vehicles based on four-wheel steering[J]. Proc Insti Mech Engi, Part D: J Autom Engi, 2025, 239(2-3): 548-563.
[4]	曹莉凌, 刘威, 代堃鹏, 等. 采用转角补偿LQR的自动驾驶集卡路径跟踪控制[J]. 汽车安全与节能学报, 2024, 15(3): 413-423.
	CAO Liling, LIU Wei, DAI Kunpeng, et al. Path tracking control of autonomous container trucks using angular compensation LQR[J]. J Autom Safe Energ, 2024, 15(3): 413-423. (in Chinese).
[5]	LIU Yujie, YUE Ming, ZHAO Xudong, et al. Adaptive robust path tracking preview control for tractor-trailer trucks considering trailer sway and stochastic disturbances[J]. IEEE Trans Intel Transport Syst, 2025, 26(4): 5422-5434.
[6]	SONG Zhongchao, ZHANG Liwei, ZHANG Menglei, et al. Research on an all-axle active steering control strategy of articulated vehicles based on feedforward-feedback control[J]. Int’l J Heav Vehi Syst, 2025, 32(2): 189-209.
[7]	Esmaeili N, Kazemi R, Tabatabaei Oreh S H. An adaptive sliding mode controller for the lateral control of articulated long vehicles[J]. Proc Instit Mech Engi, Part K: J Multi-body Dyna, 2019, 233(3): 487-515.
[8]	Farbood M, Shasadeghi M, Niknam T, et al. Cooperative H∞ robust move blocking fuzzy model predictive control of nonlinear systems[J]. IEEE Trans Sys, Man, Cybe: Sys, 2023, 53(12): 7707-7718.
[9]	LIU Jun, ZHAO Xiaoming, GUO Hongyan, et al. Tractor semi-trailer modeling and regional path tracking control strategy[C]// 2020 Chinese Autom Congr (CAC), Shanghai, China, 2020: 1961-1966.
[10]	LI Erhang, YU Huilong, XI Junqiang, et al. Stability-guaranteed model predictive path tracking of autonomous tractor semi-trailers under extreme conditions[J]. IEEE Trans Intel Vehi, 2025, 10(4): 2427-2440.
[11]	SONG Hongzheng, LI Xu. Research on path tracking control of articulated train active steering[C]// 2024 3rd Int’l Conf Robot, Artif Intelligence Intelligent Cont (RAIIC), Mianyang, China, 2024: 317-320.
[12]	Esmaeili N, Kazemi R. Development of the active distur-bance rejection control method for increasing the stability of the long articulated vehicle[J]. Proc Instit Mech Engi, Part D: J Autom Engi, 2019, 233(13): 3554-3576.
[13]	Jeong D, Choi S B. Tube-based robust model predictive control for tracking control of autonomous articulated vehicles[J]. IEEE Trans Intel Vehi, 2023, 9(1): 2184-2196.
[14]	Barbosa F M, Marcos L B, da Silva M M, et al. Robust path-following control for articulated heavy-duty vehicles[J]. Cont Engi Pract, 2019, 85: 246-256.
[15]	LIU Bing, LI Zhongmei, NIE Rong, et al. A less conservative robust control approach and its application to truck-trailer system[J]. IEEE Trans Autom Sci Engi, 2024, 22: 442-452.
[16]	TIAN Yang, MA He, MA Lei, et al. Path tracking control of commercial vehicle emergency obstacle avoidance based on MPC and active disturbance rejection control[J]. IEEE Trans Transport Elect, 2024, 11(3): 7162-7170.
[17]	张忠, 吴晓建, 江会华, 等. 多源干扰下的智能车模型预测纵向运动抗干扰控制[J]. 汽车工程, 2024, 46(10): 1816-1828.
	ZHANG Zhong, WU Xiaojian, JIANG Huihua, et al. Model predictive anti-disturbance control for longitudinal motion of intelligent vehicles under multi-source disturbances[J]. Autom Engineering, 2024, 46(10): 1816-1828. (in Chinese).
[18]	李道飞, 查安飞, 徐彪, 等. 半挂汽车列车紧急避撞轨迹跟踪控制算法[J]. 汽车工程, 2022, 44(7): 1098-1106.
	LI Daofei, CHA Aanfei, XU Biao, et al. Emergency collision avoidance trajectory tracking control algorithm of semi-trailer train[J]. Autom Engineering, 2022, 44(7): 1098-1106. (in Chinese).
[19]	YANG Wei, CAI Yingfeng, SUN Xiaoqiang, et al. Trajec-tory tracking control of autonomous vehicles based on Lagrangian neural network dynamics model[J]. Proc Instit Mech Engi, Part D: J Autom Engi, 2024, 238(12): 3483-3498.