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基于双电机线控转向系统的拖拉机轨迹跟踪控制研究

李磊1,2,3, 潘良1,2,3, 董继伟1,2,3, 曹中华1,2,3, 罗兴发1,2,3, 湛小梅1,2,3(), 李亚丽1,2,3, 孙志强1,2,3   

  1. 1. 重庆市农业科学院 农业机械研究所,重庆 401329,中国
    2. 农业农村部西南山地智慧农业重点实验室(部省共建),重庆 401329,中国
    3. 山地数智农业川渝共建重点实验室,重庆 401329,中国
  • 收稿日期:2025-07-17 出版日期:2025-10-21
  • 基金项目:
    重庆市市级财政科技创新(KYLX20240500075); 重庆市科研机构绩效激励引导专项(CSTB2024JXJL-YFX0004); 重庆市市级财政科技创新(KYLX20240500039)
  • 作者简介:

    李 磊,硕士,工程师,研究方向为智能农机。E-mail:

  • 通信作者:
    湛小梅,硕士,正高级工程师,研究方向为智能农机。E-mail:

Research on Tractor Trajectory Tracking Control Based on A Dual-Motor Steer-by-Wire System

LI Lei1,2,3, PAN Liang1,2,3, DONG Jiwei1,2,3, CAO Zhonghua1,2,3, LUO Xingfa1,2,3, ZHAN Xiaomei1,2,3(), LI Yali1,2,3, SUN Zhiqiang1,2,3   

  1. 1. Chongqing Academy of Agricultural Sciences, Institute of Agricultural Machinery Research, Chongqing 401329, China
    2. Southwest Mountain Smart Agricultural Key Laboratory (Co-construction by the Ministry and Province), Ministry of;Agriculture and Rural Affairs, Chongqing 401329, China
    3. Key Laboratory of Mountain Digital Agriculture jointly established by Sichuan and Chongqing, Chongqing 401329, China
  • Received:2025-07-17 Online:2025-10-21
  • Foundation items:Chongqing Municipal Fiscal Science and Technology Innovation(KYLX20240500075); Chongqing Scientific Research Institutions Performance Incentive and Guidance Project(CSTB2024JXJL-YFX0004); Chongqing Municipal Fiscal Science and Technology Innovation(KYLX20240500039)
  • About author:

    LI Lei, E-mail:

  • Corresponding author:
    ZHAN Xiaomei, E-mail:

摘要:

【目的】 为解决现有无人驾驶拖拉机转向系统因精度与鲁棒性不足难以满足高精度轨迹跟踪需求的问题,并克服单电机线控转向(Steer-by-Wire, SBW)系统存在的扭矩受限与容错性较差等缺陷,提出一种基于双电机SBW的轨迹跟踪控制方法。 【方法】 首先,基于拖拉机动力学模型设计线性二次调节器(Linear Quadratic Regulator, LQR),用于计算跟踪期望轨迹所需的前轮转角;其次,建立双电机SBW系统的数学模型,设计滑模控制器(Sliding Mode Controller, SMC)以实现鲁棒控制,并引入干扰观测器(Disturbance Observer, DOB)对系统扰动进行观测与补偿,从而抑制SMC的抖振现象;最后,构建一种融合LQR与SMC+DOB的分层控制架构。 【结果和讨论】 仿真与试验结果表明,所提方法在横向跟踪误差与航向角误差方面均优于未配备SBW的传统转向系统;所构建的SMC+DOB控制器与未集成DOB的SMC方法相比,在低速工况下横向平均跟踪误差、航向角误差、质心侧偏角及前轮转角误差分别降低19.9%、40.2%、6.5%和14.3%;在高速工况下分别降低23.7%、52.2%、4.5%和27.3%。 【结论】 LQR+SMC+DOB分层控制策略可有效增强转向系统的输出扭矩与容错能力,显著提升拖拉机在高、低速工况下的轨迹跟踪精度,为无人驾驶拖拉机的高性能轨迹跟踪系统设计提供了有效解决方案。

关键词: 无人驾驶拖拉机, 轨迹跟踪控制, 线控转向, LQR, SMC, 干扰观测器

Abstract:

【Objective】 Conventional steering systems for autonomous tractors often suffer from insufficient precision and robustness, making it difficult to meet the demands of high-accuracy trajectory tracking. Although single-motor steer-by-wire (SBW) systems proposed in previous studies can improve tracking precision, they are limited by restricted output torque and poor fault tolerance, especially under complex conditions such as low-speed and heavy-load operations. To address these issues, the aim is to develop a high-performance trajectory tracking control method based on a dual-motor SBW system, thereby improving tracking accuracy, robustness, and operational safety across a variety of working conditions. 【Methods】 A hierarchical control architecture was constructed in this study. At the upper level, a trajectory tracking controller was designed. Based on a two-wheel bicycle dynamic model of the tractor, a linear quadratic regulator (LQR) controller was developed to solve the optimal control problem and compute the desired front-wheel steering angle for trajectory tracking. At the lower level, a steering execution controller was developed for the dual-motor SBW system. First, a mathematical model incorporating both electrical and mechanical components of the system was established. To address nonlinearities and uncertainties, a sliding mode controller (SMC) was designed for each motor to achieve robust control. To mitigate the inherent chattering issue of SMC, a disturbance observer (DOB) was incorporated. The DOB continuously estimated the total disturbance in real time, including both external interferences and internal parameter variations, and provided feed-forward compensation. This compensation enabled a reduction in the switching gain of the SMC, thereby effectively suppressing chattering while maintaining robust performance. For dual-motor coordination, an independent control strategy was adopted, leveraging the identical parameters of both motors. Under this scheme, the two motors received the same steering angle command and operated in parallel, maintaining structural simplicity while enhancing system fault tolerance. The overall control strategy was structured as a hierarchical system, comprising an upper-level LQR trajectory tracking module and a lower-level steering execution module based on SMC+DOB. 【Results and Discussion】 To validate the effectiveness of the proposed method, both simulations and field experiments were conducted. Simulations were performed on a Matlab–Carsim co-simulation platform, specifically evaluating steering angle tracking performance and the disturbance rejection capability of the DOB. The results showed that, compared to the SMC controller without DOB, the SMC+DOB controller reduced the front-wheel steering angle tracking error by 13.12% while significantly suppressing chattering. Field experiments were carried out on a T954 tractor platform to evaluate the control strategy under realistic operating conditions. A representative working path, consisting of both straight and curved segments, was used, and tests were performed at two speeds: low speed (10 km/h) and high speed (30 km/h). To benchmark performance, three control strategies were compared: LQR+SMC, MPC+SMC+DOB, and the proposed LQR+SMC+DOB, enabling a comprehensive assessment of tracking accuracy, robustness, and real-time control effectiveness. The field experimental results show that: 1) All SBW-based methods significantly outperformed traditional steering in terms of lateral tracking error and heading error. 2) Under low-speed conditions, the proposed LQR+SMC+DOB method reduced the average lateral tracking error, heading error, sideslip angle, and front-wheel steering angle error by 19.9%, 40.2%, 6.5%, and 14.3%, respectively, compared to LQR+SMC. Under high-speed conditions, the corresponding reductions were 23.7%, 52.2%, 4.5%, and 27.3%. 3) Compared with MPC+SMC+DOB, the proposed LQR+SMC+DOB achieved similar trajectory tracking performance while significantly reducing the computation time per iteration. The above experimental results indicate that the adopted dual-motor SBW system effectively enhanced the steering output torque and system fault tolerance, thereby improving trajectory tracking accuracy and thus providing a hardware foundation for resolving the power and safety challenges in tractor heavy-load operations, and the designed SMC+DOB controller combined strong robustness with smooth control action, effectively suppressing external disturbances and markedly reducing chattering, thereby improving the accuracy and stability of steering execution. 【Conclusion】 This research successfully proposed and validated a high-precision trajectory tracking control strategy for autonomous tractors. By integrating an upper-level LQR trajectory tracking controller with a lower-level dual-motor SMC+DOB steering controller, a robust and efficient hierarchical control framework was established. This control strategy consistently exhibited excellent trajectory tracking performance and real-time capability across different speeds, offering a reliable technological solution for high-performance operations of autonomous tractors. In comparison with the MPC control strategy, the control strategy proposed in the study demonstrated exceptionally high computational efficiency, rendering it highly suitable for real-time control applications.

Key words: autonomous tractor, trajectory tracking, steer by wire, LQR, SMC, disturbance observer

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