基于P-PP的轻型商用车路径跟踪控制

doi:10.19562/j.chinasae.qcgc.2025.04.008

Abstract

Abstract:

To improve the accuracy and stability of path tracking for light commercial vehicles under complex curvature conditions， in this paper a Predictive-Pure Pursuit （P-PP） control method is proposed. Firstly， a P-PP controller is designed based on the vehicle's discrete kinematic model， and a PID compensator is developed based on heading error to enhance tracking accuracy and stability. Secondly， to address the challenge of maintaining both accuracy and stability under complex curvature conditions with a fixed prediction horizon algorithm， a variable prediction horizon optimization algorithm is proposed. A cost function based on the lateral and curvature errors within the prediction horizon is established， and Bayesian optimization is used to determine the optimal prediction horizon， resolving the conflict between accuracy and stability. Finally， TruckSim/Simulink co-simulation and real vehicle tests are conducted. In the real vehicle tests， the root mean square values of the lateral error， heading error， and steering wheel angle for the Bayesian-optimized P-PP controller is 0.113 m， 0.045 rad， and 153.2°， respectively， all of which are superior to the corresponding metrics of the P-PP controller based on fuzzy control and the MPC controller， indicating that the proposed controller maintains good precision and stability under complex curvature conditions.

Key words: light commercial vehicles, path tracking, P-PP, PID compensation, Bayesian optimization

Zhihong Wang,Jiarong Zeng,Jie Hu,Zhiling Zhang,Donghao Yang,Yuefeng Ji. Path Tracking Control of Light Commercial Vehicles Based on P-PP[J].Automotive Engineering, 2025, 47(4): 669-679.

Figures/Tables 14

算法1：参数优化
1：Input：初始化 $θ 0$ ，以及对应的代价函数 $J 0$
2： $θ, J ← θ 0, J 0$
3：procedure
4：for $i = 1,2, ?, N$ do
5： $μ ˉ, H ˉ ← G P λ, θ, J$
6： $θ i = a r g ? m a x ? E I (θ)$
7： $θ i$ 更新控制器参数，计算 $J i$
8： $θ, J ← θ 0, J 0 ? θ i, J i$
9：end for
10： $θ * ← a r g ? m i n J 0, J 1, ?, J N$
11：return $θ *$
12：end procedure

参数	数值
整车质量 $m / k g$	2 500
前轴到质心距离 $l f ? / m$	1.35
后轴到质心距离 $l r ? / m$	3.05
绕z轴转动惯量 $I z ? / (k g ? m 2)$	4 116
前轴侧偏刚度 $C α f / (N ? r a d - 1)$	178 000
后轴侧偏刚度 $C α r / (N ? r a d - 1)$	178 000
转向传动比 $i p$	25.0
离散时间 $d t / s$	0.1
$k p$	3
$k i$	0.02
$k d$	0.4
贝叶斯优化参数 $θ$ 范围	［1，10］
贝叶斯优化迭代次数	15
性能目标权重矩阵 $Q$	diag［20，10］

参数	数值
整车质量 $m / k g$	14 440
前轴到质心距离 $l f ? / m$	2.93
后轴到质心距离 $l r ? / m$	1.57
绕z轴转动惯量 $I z ? / (k g ? m 2)$	60 000
前轴侧偏刚度 $C α f / (N ? r a d - 1)$	400 000
后轴侧偏刚度 $C α r / (N ? r a d - 1)$	400 000
转向传动比 $i p$	20.7
离散时间 $d t / s$	0.1
$k p$	1.5
$k i$	0.05
$k d$	0.2
贝叶斯优化参数 $θ$ 范围	［1，5］
贝叶斯优化迭代次数	10
性能目标权重矩阵 $Q$	diag［35，15］

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参数	模糊P-PP	MPC	贝叶斯P-PP
横向误差均方根/m	0.121	0.128	0.113
航向误差均方根/rad	0.048	0.050	0.045
转向盘转角均方根/（°）	155.6	157.5	153.2

参数	未使用PID补偿器	使用PID补偿器
横向误差均方根/m	0.118	0.113
航向误差均方根/rad	0.046	0.045

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Path Tracking Control of Light Commercial Vehicles Based on P-PP

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Figures/Tables 14

References 24

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