基于数据驱动的车辆编队分布式预测控制

doi:10.13229/j.cnki.jdxbgxb.20250574

Abstract

Abstract:

An online data-driven predictive control method was proposed， which constructs an equivalent linear model of the controlled plant at each discrete time step of the closed-loop operation， and a recursive least squares algorithm with a dynamically adjusted forgetting factor based on parameter identification residuals was developed to estimate the model's time-varying parameters online. Furthermore， a distributed predictive control strategy was proposed based on a predecessor-leader-follower communication topology. The strategy decomposes the global optimization problem of the vehicle platoon into local subproblems for each follower vehicle， enabling parallel computation and improving solution efficiency. Co-simulation results using TruckSim and Matlab/Simulink demonstrate that the proposed data-driven model accurately captures vehicle dynamics across time-varying environments， and the designed distributed controller ensures effective longitudinal tracking and lateral lane-keeping performance of the platoon.

Key words: vehicle platoon, data-driven control, lane-keeping, distributed predictive control

CLC Number:

TP273

Shu-you YU,Ze-peng LIU,Bao-jun LIN,Hong CHEN. Data⁃driven distributed predictive control of vehicle platoons[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(9): 3056-3068.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Parameters of the ith vehicle"

参数	数值	参数	数值
$m i$ /kg	18 000	$I i z$ /（kg·m²）	130 421.8
$l f$ /m	3.5	$l r$ /m	1.5
$J i f$ /（kg·m²）	24	$J i r$ /（kg·m²）	48
$R e$ /m	0.51	$ρ$ /（kg·m^-3）	1.225 8

Table 1

Fig.6

Table 2

Controller parameters"

参数	RLS-DPC	NMPC
T_s	0.01 s	0.01 s
N_p	7	7
预瞄距离	5 m	5 m
$d d e s$	11 m	11 m
$x n$	4	—
$y n$	2	—
$δ i, m i n, δ i, m a x$	$- 0.1, ? 0.1 ? r a d$	$- 0.1, ? 0.1 ? r a d$
$T i, m i n d, T i, m a x d$	$- 1 E 4, ? 1 E 4 ? N ? m$	$- 1 E 4, ? 1 E 4 ? N ? m$
$e i, m i n p, ? e i, m a x p$	$- 5, ? 5 ? m$	$- 5, ? 5 ? m$
$e i, m i n y, ? e i, m a x y$	$- 0.5, ? 0.5 ? m$	$- 0.5, ? 0.5 ? m$
$e i, m i n φ, ? e i, m a x φ$	$- 0.15, ? 0.15 ? r a d$	$- 0.15, ? 0.15 ? r a d$
$Q$	$d i a g (2 E 6,1 E 7,3 E 2,3 E 2)$	$d i a g (6 E 6,2 E 8,3 E 7,3 E 7)$
$R$	$d i a g (4 E 1,3 E 1)$	$d i a g (5 E - 3,1 E 8)$

Table 2

Fig.7

Fig.8

Fig.9

Table 3

Fig.10

Fig.11

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算法	跟随车1	跟随车2	跟随车3
RLS-DPC	0.002 9	0.002 4	0.002 3
NMPC	0.032 3	0.029 8	0.029 8

Data⁃driven distributed predictive control of vehicle platoons

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