Department of Automobile and Traffic Engineering, Wuhan University of Science and Technology, No. 2, Huangjiahu West Road, Hongshan District, Wuhan 430065, China.
Department of Automobile Engineering, Hubei University of Automotive Technology, Shiyan 442002, China.
Sensors (Basel). 2022 Jul 10;22(14):5163. doi: 10.3390/s22145163.
Most existing Path-Following Algorithms (PFAs) are developed for single-unit vehicles (SUVs) and rarely for articulated vehicles (AVs). Since these PFAs ignore the motion of the trailer, they may cause large tracking deviations and ride stability issues when cornering. To this end, an Adaptive Articulation Angle Preview-based Path-Following Algorithm (AAAP-PFA) is proposed for AVs. Different from previous PFAs, in this model, a simple linear vehicle dynamics model is used as the prediction model, and an offset distance calculated by an articulation angle is used as part of the preview distance. An adaptive posture control strategy is designed to trade off the trajectory tracking performance and lateral stability performance during the path-following process. Considering a large prediction mismatch caused by using a linear vehicle dynamics model, a feedback correction method is proposed to improve the robustness of the steering control. In the comparison simulation experiment with SUV-PFA, it is confirmed that the novel PFA has better adaptability to the contradictory relationship between tracking performance and lateral stability and has strong steering control robustness.
大多数现有的路径跟踪算法（PFAs）都是针对单体车辆（SUV）开发的，很少针对铰接车辆（AV）。由于这些 PFAs 忽略了拖车的运动，因此在转弯时可能会导致较大的跟踪偏差和行驶稳定性问题。为此，针对 AV 提出了一种基于自适应铰接角预测的路径跟踪算法（AAAP-PFA）。与以前的 PFAs 不同，在该模型中，使用简单的线性车辆动力学模型作为预测模型，并使用铰接角计算的偏移距离作为预视距离的一部分。设计了一种自适应姿态控制策略，以平衡路径跟踪过程中的轨迹跟踪性能和横向稳定性性能。考虑到使用线性车辆动力学模型会导致较大的预测失配，提出了一种反馈校正方法来提高转向控制的鲁棒性。在与 SUV-PFA 的比较仿真实验中，证实了新型 PFA 对跟踪性能和横向稳定性之间的矛盾关系具有更好的适应性，并且具有很强的转向控制鲁棒性。
