基于路径跟踪和铰接铰链力优化的虚拟轨道列车牵引引导协同控制研究

Zehan Wang, Zhenggang Lu
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引用次数: 0

摘要

虚拟轨道列车(VTT)是一种采用胶轮和轨道交通管理模式的新型城市公共交通系统。它的全轮驱动全轴转向(AWDAAS)特性使这种多关节车辆能沿着目标路径(虚拟轨道)高精度运行。本文提出了一种新的牵引引导协同控制策略,旨在实现路径跟踪和铰链力优化。首先,采用两步法建立车辆动力学模型,主要包括车轮转向角之间的重心(CG)力模型、扭矩和每个单元的广义力。其次,以 CG 广义力作为控制输入建立车辆多体动力学模型,并对三单元六轴 AWDAAS-VTT 进行具体建模。这种建模方法可以满足根据不同的编组数量和车轴进行快速扩展和重构。然后通过车辆状态和控制输入对铰链力进行分析和定量表达。提出了一种基于分层框架的牵引引导协同控制策略,主要包括三个部分:路径跟踪和纵向速度目标的协同分配、基于改进的模型预测控制(MPC)和斯坦利算法的各单元 CG 通用力计算以及车轮扭矩和角度的控制分配(CA)。控制策略通过协同仿真进行了验证。结果表明,所提出的控制策略可以实现出色的跟踪精度,并有效降低铰链力。此外,协同控制下的车辆还具有出色的弯道通过稳定性和连续弯道适应性。该控制策略在不同的运行速度、弯道半径和有效载荷下都具有很高的鲁棒性。即使在以 14 米/秒的速度通过 R20 米曲线的极端条件下,最大跟踪误差也小于 0.035 米。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on traction guidance cooperative control of virtual track train based on path-tracking and articulation hinge forces optimisation
Virtual track train (VTT) is a new urban public transportation system that adopts rubber wheel and rail transit management mode. Its characteristics of all-wheel-driving all-axle-steering (AWDAAS) enable this multi-articulated vehicle to run along the target path (virtual track) with high precision. This paper proposes a new traction guidance cooperative control strategy aiming at path-tracking and hinge forces optimisation. Firstly, the vehicle dynamics model is established using the two-step method, which mainly includes the centre of gravity (CG) forces model between the wheel steering angles, torques and the generalised forces at every unit. Secondly, the vehicle multi-body dynamics model is built with the CG generalised forces as the control input and carries on the concrete modelling for the three-unit six-axle AWDAAS-VTT. This modelling method can satisfy the rapid expansion and reconstruction according to the different marshalling numbers and axles. Then the hinge forces are analysed and expressed quantitatively by vehicle state and control inputs. A traction guidance cooperative control strategy based on a hierarchical framework is proposed, which has mainly three parts: the cooperative distribution of path-tracking and longitudinal speed targets, the CG generalised forces calculation of every unit based on improved model predictive control (MPC) and Stanley algorithm, and the control allocation (CA) of wheel torque and angle. The control strategy is verified by Co-simulation. The results show that the proposed control strategy can achieve excellent tracking accuracy and effectively reduce hinge forces. In addition, the vehicle under cooperative control also has excellent curve passing stability and continuous curve adaptability. The control strategy has high robustness under different running speeds, curve radii and payloads. Even under the extreme condition of passing through the R20 m curve at 14 m/s, the maximum tracking error is less than 0.035 m.
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