约束与时滞影响下的重型矿用卡车路径跟踪控制

Path tracking control of heavy mining trucks under constraint and time delay

摘要

无人驾驶重型矿用卡车具有明显的转向机构约束和较长的信号时滞，在急弯与时滞共同作用下路径跟踪性能容易下降甚至失控. 针对急弯场景中系统响应滞后造成的误差增大问题，引入参考路径未来航向并结合关键参考点位移误差构建预瞄纠偏结合控制算法（Preview-correct control, PCC），提升入弯与出弯阶段的跟踪精度. 针对信号时滞导致的控制执行偏差，基于 PCC 输出结构与重型矿用卡车动力学特性，设计了多步运动补偿型时滞补偿器，用于预测时滞期间的车辆运动状态. 最终将 PCC 与时滞补偿器整合，构建面向重型矿用卡车的路径跟踪控制系统. 经过空载与满载两种工况的仿真测试与实车实验验证，所构建的控制系统在转向约束和信号时滞较为显著的条件下表现出较高精度和实时性. 在 20 km·h⁻¹ 空载仿真中，PCC 的位移误差最大幅值为 0.0892 m，而 PID（Proportional-integral-derivative）、预瞄 PID 等控制方法在入弯后误差发散，同时，PCC 的实时性指标明显优于非线性模型预测控制. 在满载且存在 0.4 s 时滞的情况下，结合时滞补偿器的 PCC 系统将位移误差最大幅值控制在 0.1537 m，而未补偿的 PCC 出现误差发散. 两组实车实验的位移误差最大幅值分别为 0.1976 m 和 0.2073 m，车辆均能稳定通过急弯，无失控情况. 结果表明，该控制系统能够在真实约束与长时滞场景下显著提升重型矿用卡车路径跟踪性能，具备工程应用与部署价值.

Abstract

Heavy mining trucks are key equipment in open-pit haulage systems, where the available roadway space is often narrow in relation to the vehicle’s size, resulting in extremely difficult driving. With the rapid advancement of mining intelligence, autonomous-driving technology has become an essential means of improving production efficiency to ensure operational safety and reduce operating costs. As a core component of autonomous-driving systems, path tracking control plays a decisive role in ensuring stable vehicle motion along a reference path. However, heavy mining trucks exhibit pronounced steering-mechanism constraints and significant signal transmission delays. Under the combined influence of sharp curves and long delays, path tracking systems tend to exhibit sluggish responses that rapidly increase tracking errors and even instability. Existing control methods struggle to simultaneously handle the compound effects of steering constraints and time delay, limiting their engineering applicability. To address the response lag caused by front-wheel steering-rate constraints in sharp-curve environments, a preview correct control (PCC) algorithm was developed by introducing the future heading of the reference path as preview information and incorporating the keypoint displacement error. The preview component improves steering proactiveness, while the correction component enhances responsiveness to current deviations to enable stable posture adjustments during curve entry, mid-curve, and exit. The PCC does not rely on complex models or high-performance computing platforms, making it suitable for the real-time operation of low-power onboard controllers. To address signal transmission delays in autonomous-driving systems, a multistep motion-compensation delay compensator is established by analyzing the PCC output structure and dynamic characteristics of a heavy mining truck to predict the vehicle’s posture evolution during the delay interval and generate new control inputs that counteract the delay effects. By integrating the PCC with the delay compensator, a path tracking control system capable of simultaneously handling steering mechanism constraints and long delays was achieved for heavy mining trucks. Simulations were conducted under no-load and full-load conditions, followed by full-load field experiments. In no-load simulations at 20 km·h⁻¹ on a U-shaped curve with a radius of 35 m, the PCC achieved a maximum displacement error of 0.0892 m, which is significantly more accurate than proportional-integral-derivative (PID) and preview PID and close to the nonlinear model predictive control (NMPC). Its average computation time was only 0.1514 ms, outperforming NMPC in terms of real-time capability. Under fully loaded conditions with a 0.4 s signal delay, the PCC combined with the delay compensator maintained the maximum displacement error within 0.1537 m, while the uncompensated PCC showed error divergence in sharp-curve sections. This demonstrates the critical role of the proposed compensation strategy in ensuring system stability under long-delay conditions. The compensator increased the average computation time by only 0.0982 ms, which had a negligible impact on real-time performance. Two sets of full-load field tests were conducted, with an actual signal delay of approximately 0.4 s. The maximum displacement errors were 0.1976 and 0.2073 m. In both tests, the vehicle navigated the sharp curve stably, without any loss of control or noticeable yaw deviations. Overall, the simulation and experimental results demonstrate that the proposed control system maintained a stable and reliable path tracking performance under significant steering-mechanism constraints and long signal delays, achieving a favorable balance between accuracy, real-time capability, and engineering deployability. Therefore, it is well suited for practical autonomous-driving applications in heavy mining trucks.

关键词

矿用卡车 / 路径跟踪 / 预瞄控制 / 信号时滞 / 约束

Key words

mining trucks / path tracking / preview control / signal time delay / constraints

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基金资助

国家重点研发计划资助项目(2023YFC3806603)

国家自然科学基金资助项目(52202505)

中国博士后科学基金资助项目(2022M710354)

唐山市人才资助项目(C202503022)