多轴轮式铰接特种车辆双策略轨迹跟踪控制

doi:10.12382/bgxb.2024.0954

摘要/Abstract

摘要：

多轴轮式铰接特种车辆在轨迹跟踪时易产生尾部放大效应和侧倾等不稳定性问题,研究多轴轮式铰接车辆的稳定性轨迹跟踪具有重要意义。以只有拖车具备转向能力的多轴轮式铰接车辆为研究对象,建立7自由度车辆动力学模型,提出双策略轨迹跟踪控制方法,上层策略求解拖车轨迹跟踪问题,下层策略优化挂车轨迹跟踪问题,兼顾了拖车和挂车的轨迹跟踪精度。为保证控制策略的计算实时性,利用有限时域近似动态规划近似求解上层轨迹跟踪策略,将在线优化问题转化为神经网络参数的离线预求解,降低在线求解耗时。与高保真度仿真软件的联合仿真实验表明:新方法使多轴铰接车辆轨迹跟踪精度提升了12.82%,策略单步求解耗时低于10ms,计算效率相比模型预测控制算法提升了约3个数量级。

关键词: 自动驾驶, 轨迹跟踪控制, 近似动态规划, 双层优化

Abstract:

Multi-axle articulated wheeled vehicles are prone to occurring “tail amplification” effects and lateral instability during trajectory tracking.The study of stable trajectory tracking for multi-axle articulated wheeled vehicles is of significant importance.For the multi-axle articulated wheeled vehicle,a 7-degree-of-freedom vehicle dynamics model is established,and a bi-level strategy trajectory tracking control method is proposed.The upper-level strategy is used to address the tractor trajectory tracking problem,while the lower-level strategy is used to optimize the trailer trajectory tracking,ensuring precise tracking for both tractor and trailer.To guarantee the real-time computation of the control strategy,the upper-level trajectory tracking strategy is solved using a finite-horizon approximate dynamic programming approach,and the online optimization problem is transformed into an offline pre-solution of parameters,thus reducing the time required for online solving.This approach significantly reduces online computation time.Co-simulation experiments with high fidelity simulation software demonstrate that the proposed method is used to improve the trajectory tracking accuracy of multi-axle articulated wheeled vehicles by 12.82%,and keep a single-step solution time below 10ms,enhancing computational efficiency by three orders of magnitude compared with the model predictive control algorithm.

Key words: self-driving, trajectory tracking, approximate dynamic programming, bi-level optimization

中图分类号:

TJ810.1

张发旺, 陈良发, 段京良, 刘辉, 聂士达, 张晨. 多轴轮式铰接特种车辆双策略轨迹跟踪控制[J]. 兵工学报, 2025, 46(8): 240954-.

ZHANG Fawang, CHEN Liangfa, DUAN Jingliang, LIU Hui, NIE Shida, ZHANG Chen. Bi-level Strategy Trajectory Tracking Control of Multi-axle Articulated Wheeled Vehicle[J]. Acta Armamentarii, 2025, 46(8): 240954-.

图/表 10

图1 多轴轮式铰接车辆受力分析

Fig.1 Force analysis of multi-axle articulated wheeled vehicle

图2 双策略轨迹跟踪控制

Fig.2 Bi-level strategy trajectory tracking control

图3 方法伪代码

Fig.3 Method pseudo-code

表1 训练参数

Table 1 Training Parameters

参数	数值	参数	数值	参数	数值
η_θ	1×10^-5	N	100	M	20
η_ω	3×10^-5	Δt/s	0.01	K	50000

图4 下层策略控制目标函数值

Fig.4 Lower-level strategy control objective function value

图5 单车道变换工况状态、控制曲线

Fig.5 Single lane change state and control curve

图6 稳态圆周行驶工况状态、控制曲线

Fig.6 Circular driving state and control curve

表2 单车道变换工况车辆最大误差

Table 2 Maximum error in single lane change

方法	拖车横向误差/m	拖车横摆角误差/(°)	挂车横向误差/m	挂车横摆角误差/(°)
MPC	0.064	1.15	0.077	1.38
FHADP	0.078	1.61	0.078	1.32
Bi-level	0.073	1.55	0.068	1.32

表3 仿真工况单步求解平均耗时

Table 3 Average calculation time per step for simulation task ms

工况	MPC方法	FHADP方法	Bi-level方法
单车道变换	1510.23	9.15	7.49
稳态圆周行驶	4075.99	6.67	6.04

表4 稳态圆周行驶工况车辆状态最大绝对误差

Table 4 Maximum absolute errors of vehicle state in circular driving

方法	拖车横向误差/m	拖车横摆角误差/(°)	挂车横向误差/m	挂车横摆角误差/(°)
MPC	1.12	6.31	0.40	4.59
FHADP	1.05	5.73	0.42	5.16
Bi-level	0.92	5.73	0.40	4.59

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