Research on adaptive trajectory tracking control method for intelligent vehicle

doi:10.3969/j.issn.1674-8484.2025.02.014

Abstract

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

CLC Number:

U461

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.

Figures/Tables 12

References 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