白国星, 罗维东, 刘立, 孟宇, 顾青, 李凯伦. 矿用铰接式车辆路径跟踪控制研究现状与进展[J]. 工程科学学报, 2021, 43(2): 193-204. DOI: 10.13374/j.issn2095-9389.2020.07.14.003
引用本文: 白国星, 罗维东, 刘立, 孟宇, 顾青, 李凯伦. 矿用铰接式车辆路径跟踪控制研究现状与进展[J]. 工程科学学报, 2021, 43(2): 193-204. DOI: 10.13374/j.issn2095-9389.2020.07.14.003
BAI Guo-xing, LUO Wei-dong, LIU Li, MENG Yu, GU Qing, LI Kai-lun. Current status and progress of path tracking control of mining articulated vehicles[J]. Chinese Journal of Engineering, 2021, 43(2): 193-204. DOI: 10.13374/j.issn2095-9389.2020.07.14.003
Citation: BAI Guo-xing, LUO Wei-dong, LIU Li, MENG Yu, GU Qing, LI Kai-lun. Current status and progress of path tracking control of mining articulated vehicles[J]. Chinese Journal of Engineering, 2021, 43(2): 193-204. DOI: 10.13374/j.issn2095-9389.2020.07.14.003

矿用铰接式车辆路径跟踪控制研究现状与进展

Current status and progress of path tracking control of mining articulated vehicles

  • 摘要: 铰接式车辆的路径跟踪控制是矿山自动化领域中的关键技术,而数学模型和路径跟踪控制方法是铰接式车辆路径跟踪控制中的两项重要研究内容。在数学模型研究中,铰接式车辆的无侧滑经典运动学模型较为适合作为低速路径跟踪控制的参考模型,而有侧滑运动学模型作为参考模型时则可能导致侧滑加剧。此外基于牛顿–欧拉法建立的铰接式车辆四自由度动力学模型原则上满足路径跟踪控制的需求,但是还需要解决当前的四自由度模型无法同时反映瞬态转向特性和稳态转向特性的问题。在路径跟踪控制方法研究中,反馈线性化控制、最优控制、滑模控制等无前馈信息的控制方法无法有效解决铰接式车辆跟踪存在较大幅度曲率突变的参考路径时误差较大的问题,前馈–反馈控制可以用于解决上述问题,但是在参考路径具有不同幅度的曲率突变时需要解决自动调整预瞄距离的问题,而模型预测控制,尤其是非线性模型预测控制,可以更加有效地利用前馈信息,且不需要考虑预瞄距离的设置,从而可以有效提高铰接式车辆跟踪存在较大幅度曲率突变的参考路径时的精确性。此外,对于基于非线性模型预测控制的铰接式车辆路径跟踪控制,还需深化三个方面的研究。首先,该控制方法仍然存在误差最大值随参考速度增大而增加的趋势。其次,目前该控制方法以运动学模型作为预测模型,无法解决铰接式车辆以较高的参考速度运行时侧向速度导致的精确性下降和安全性恶化的问题。最后,还需对这种控制方法进行实时性方面的优化研究。

     

    Abstract: Path tracking control of articulated vehicles is a focus in the field of mine automation. Mathematical models and path tracking control methods are two key focal points of research in this area. For mathematical models of articulated vehicles, the classic kinematics model without side-slip is suitable as a reference model for low-speed autonomous driving control. However, when this model is used as the reference model, it may lead to an intensification of sliding. In any event, the four-degree-of-freedom dynamic model of articulated vehicles based on the Newton–Euler method meets the requirements of autonomous driving control in principle. However, this model cannot reflect both transient and steady steering characteristics. In the research of path tracking control methods, feedback linearization control, optimal control, sliding mode control, and other control methods without feedforward information cannot effectively solve the problem of a large error when vehicle tracking a reference path with large abrupt changes in curvature. Feedforward–feedback control can be used to solve the above problem, but when the reference path has diverse amplitudes of abrupt changes in curvature, it is necessary to automatically adjust the preview distance. Model predictive control, especially nonlinear model predictive control, can use feedforward information more effectively and does not need to consider the setting of the preview distance. This way, when the articulated vehicle tracks a reference path with large abrupt changes in curvature, accuracy can be effectively improved. Additionally, for the path tracking control of articulated vehicles based on nonlinear model predictive control, three aspects of research need to be deepened. First, for this control method, there is still a trend that the maximum value of the error increases as the reference velocity increases. Second, currently, this control method uses the kinematics model as the prediction model, so it cannot solve the twin problems of reduced accuracy and worsened safety, caused by the lateral velocity when the articulated vehicle runs at a higher reference velocity. Finally, real-time optimization research on this control method is needed.

     

/

返回文章
返回