智能车辆自适应轨迹跟踪控制方法研究

doi:10.3969/j.issn.1674-8484.2025.02.014

摘要/Abstract

摘要：

针对智能车辆在变速度和变路面附着系数工况时轨迹跟踪精度和操纵稳定性差的问题，设计了一种基于模型预测控制（MPC）的自适应轨迹跟踪控制方法。基于侧向力滑模观测器和魔术轮胎逆模型设计轮胎等效侧偏刚度估计方法，实时修正动力学模型参数；制定了兼顾路面附着系数和行驶车速的动态预测时域控制策略，建立了自适应MPC的轨迹跟踪控制器；通过Simulink-CarSim联合仿真验证在变附着系数路面变速双移线工况下该方法的有效性。结果表明：与传统MPC控制方法相比，该文设计的方法在高附着系数路面中高速变速行驶时，操纵稳定性得以改善，略微牺牲跟踪精度，平均横摆角速度能改善19.82%；在变附着系数路面低中速变速行驶时平均横向偏移量和平均横摆角速度分别降低了84.90%和46.23%，能够有效改善轨迹跟踪控制精度和操纵稳定性。

关键词: 轨迹跟踪, 模型预测控制（MPC）, 侧偏刚度估计, 变预测时域

Abstract:

Aiming at the problem of poor trajectory tracking accuracy and handling stability of intelligent vehicles under variable speed and variable road adhesion coefficient conditions, an adaptive trajectory tracking control method based on model predictive control (MPC) was designed. Based on the lateral force sliding mode observer and the inverse model of magic tire, the tire equivalent cornering stiffness estimation method was designed to correct the dynamic model parameters in real time. A dynamic predictive time-domain control strategy that took into account the road adhesion coefficient and driving speed was developed, and an adaptive MPC trajectory tracking controller was established. The effectiveness of the adaptive model predictive control method was verified by Simulink-CarSim joint simulation under the conditions of double lane change with variable speed and road adhesion coefficient compared with the traditional MPC control method. The results show that compared with the traditional MPC control method, the control stability of the proposed method is improved at high speed and variable speed on the high adhesion coefficient road, and the average yaw speed is improved by 19.82% at a slight sacrifice of tracking accuracy. The average lateral offset and yaw velocity are reduced by 84.90% and 46.23% respectively when driving at medium and low speed on the road surface with variable adhesion coefficient, which can effectively improve the trajectory tracking control accuracy and handling stability.

Key words: trajectory tracking, model predictive control (MPC), tire cornering stiffnesses estimation, variable prediction time domain

中图分类号:

U461

张硕, 李潇, 陈轶嵩, 赵轩, 余强, 余曼. 智能车辆自适应轨迹跟踪控制方法研究[J]. 汽车安全与节能学报, 2025, 16(2): 303-314.

ZHANG Shuo, LI Xiao, CHEN Yisong, ZHAO Xuan, YU Qiang, YU Man. Research on adaptive trajectory tracking control method for intelligent vehicle[J]. Journal of Automotive Safety and Energy, 2025, 16(2): 303-314.

图/表 12

参考文献 27

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预测时域	8~29
控制时域	2
采样周期	0.02 s
前轮转角控制量	-δ_{f, max}~δ_{f, max}
前轮转角控制增量	-0.847°~0.847°
Q	$\left[\begin{array}{cc} 1000 & 0 \\ 0 & 600 \end{array}\right]$
R	100
ρ	1 000

预测时域	8~29
控制时域	2
采样周期	0.02 s
前轮转角控制量	-δ_{f, max}~δ_{f, max}
前轮转角控制增量	-0.847°~0.847°
Q	$\left[\begin{array}{cc} 1000 & 0 \\ 0 & 600 \end{array}\right]$
R	100
ρ	1 000

	N_p	轨迹跟踪的有效性	\|E_d\|_max m	\|E_ψ\|_max (°)	\|β\|_max (°)	\|ω\|_max [(°)·s^-1]
60，0.8	8	有效	0.071 7	0.759 4	1.528 3	21.081 7
	15	有效	0.158 5	1.573 0	1.243 7	16.964 6
	22	有效	0.345 2	2.484 5	0.746 5	12.401 3
	29	有效	0.479 5	2.885 2	0.583 4	10.172 7
60，0.3	8	失败	-	-	-	-
	15	失败	-	-	-	-
	22	有效	0.362 2	2.221 0	1.682 0	11.935 5
	29	有效	0.545 7	3.021 0	0.727 2	10.250 6
30，0.8	8	有效	0.065 5	2.495 3	0.870 8	8.552 3
	15	有效	0.126 2	2.767 0	0.765 4	7.534 6
	22	有效	0.257 2	3.333 1	0.657 3	6.512 1
	29	有效	0.421 9	3.851 4	0.536 8	5.370 4
30，0.3	8	有效	0.040 1	2.394 0	0.909 1	8.307 0
	15	有效	0.123 9	2.821 2	0.820 4	7.530 1
	22	有效	0.250 5	3.364 2	0.814 9	7.477 1
	29	有效	0.468 7	3.857 2	0.571 6	5.256 1

	N_p	轨迹跟踪的有效性	\|E_d\|_max m	\|E_ψ\|_max (°)	\|β\|_max (°)	\|ω\|_max [(°)·s^-1]
60，0.8	8	有效	0.071 7	0.759 4	1.528 3	21.081 7
	15	有效	0.158 5	1.573 0	1.243 7	16.964 6
	22	有效	0.345 2	2.484 5	0.746 5	12.401 3
	29	有效	0.479 5	2.885 2	0.583 4	10.172 7
60，0.3	8	失败	-	-	-	-
	15	失败	-	-	-	-
	22	有效	0.362 2	2.221 0	1.682 0	11.935 5
	29	有效	0.545 7	3.021 0	0.727 2	10.250 6
30，0.8	8	有效	0.065 5	2.495 3	0.870 8	8.552 3
	15	有效	0.126 2	2.767 0	0.765 4	7.534 6
	22	有效	0.257 2	3.333 1	0.657 3	6.512 1
	29	有效	0.421 9	3.851 4	0.536 8	5.370 4
30，0.3	8	有效	0.040 1	2.394 0	0.909 1	8.307 0
	15	有效	0.123 9	2.821 2	0.820 4	7.530 1
	22	有效	0.250 5	3.364 2	0.814 9	7.477 1
	29	有效	0.468 7	3.857 2	0.571 6	5.256 1

v_x (km·h^-1)	N_p					S_i_{, max}
v_x (km·h^-1)	μ = 0.1	0.3	0.5	0.7	0.9	μ = 0.1	0.3	0.5	0.7	0.9
10	8	8	8	8	8	0.138 9	0.140 8	0.141 2	0.142 7	0.143 6
30	14	11	11	11	8	0.164 1	0.152 1	0.158 1	0.152 2	0.154 3
40	29	14	14	11	8	0.451 2	0.158 7	0.160 2	0.164 4	0.158 9
50		20	14	14	11		0.180 3	0.169 3	0.167 5	0.164 9
60		29	23	20	17		0.365 1	0.313 3	0.186 3	0.173 1
90			29	26	20			1.000 0	0.269 9	0.203 3
110				29	26				0.410 1	0.550 1
120				29	29				0.605 5	1.000 0