LTV-MPC-based Real-time and Anti-noise Motion Control for High-speed Vehicle

doi:10.12382/bgxb.2023.0979

Abstract

Abstract:

A two-layer control architecture is proposed for the real-time high-precision motion control of high-speed autonomous vehicles, in which an upper layer consists of a path point filter based on a path point Cost function and a velocity planner based on tire force analysis in both lateral and longitudinal directions, and a lower layer consists of a path tracking controller predicted by a linear time-varying dynamic model and a velocity controller. A least mean square (LMS) adaptive state estimator is introduced to enhance the system’s noise immunity. The path point filter improves the operation speed and reduces the loss of crucial information during selection, and the velocity planner generates the optimal speed curve under the premise of safe driving. The path tracking controller considers the tracking deviation soft constraint to improve the tracking effect. The LMS state estimator estimates the lateral velocity and yaw rate online based on an online-corrected dynamic model. A dSPACE-TX2 hardware-in-the-loop simulation environment is constructed, and the proposed path tracking architecture is compared with traditional motion tracking control under high-speed and double lane change scenarios. The hardware-in-the-loop simulation results demonstrate that the proposed motion controlling architecture improves the noise immunity and the tracking accuracy of 21% while meeting the 50Hz high-frequency control requirement.

Key words: linear time-varying model, model predictive control, real-time motion control, path tracking, quadratic programming

CLC Number:

U469.79

REN Hongbin, SUN Jiyu, Chih-Keng CHEN, ZHAO Yuzhuang, YANG Lin. LTV-MPC-based Real-time and Anti-noise Motion Control for High-speed Vehicle[J]. Acta Armamentarii, 2024, 45(12): 4311-4322.

Figures/Tables 22

Fig.1 Single-track vehicle model

Fig.2 Two-layer architecture vehicle motion control system

Table 1 Risk classification of road conditions

χ_i	风险等级描述	χ_i	风险等级描述
0	低风险	2	较高风险
1	一般风险	3	高风险

Fig.3 Acceleration-curvature-speed relation

Fig.4 LMS state estimator algorithm framework

Fig.5 Hardware-in-the-loop simulation environment

Table 2 Simulated vehicle parameters

参数	数值	参数	数值
L_f/m	1.015	B_pr	10
L_r/m	1.895	C_pf	1.35
m/kg	1270	C_pr	1.3
I_z/kgm²	1536.7	D_pf	7700
C_f/(N·rad^-1)	78944	D_pr	4341
C_r/(N·rad^-1)	44493	E_pf	-1.5
B_pf	10	E_pr	-1.5

Table 3 Simulated MPC parameters

参数	数值	参数	数值
$q v y$	0.5	e_y_max/m	0.05
$q e y$	2	e_φ_max/(°)	5
$q e φ$	1	N_p	40
q_δ	100	N_c	20
$q s e y$	500	T/s	0.02
$q s e φ$	500	f/Hz	50
δ_max/(°)	30	ε_w	0.99
Δδ_max/((°)·s^-1	100	ε_h	0.008

Table 3 Simulated MPC parameters

参数	数值	参数	数值
$q v y$	0.5	e_y_max/m	0.05
$q e y$	2	e_φ_max/(°)	5
$q e φ$	1	N_p	40
q_δ	100	N_c	20
$q s e y$	500	T/s	0.02
$q s e φ$	500	f/Hz	50
δ_max/(°)	30	ε_w	0.99
Δδ_max/((°)·s^-1	100	ε_h	0.008

Fig.6 Highway curvature

Table 4 Parameters of path point filter and velocity planner

参数	数值	参数	数值
w_κ	0.5	K_safe	0.823
w_d	2	μ	0.85
ω_χ	1

Fig.7 State variables before and after processing by LMS state estimator

Fig.8 Tracking deviation with and without LMS state estimator

Fig.9 Motion control results under the condition of highway

Fig.10 Tracking deviation under the condition of highway

Fig.11 Highway condition yaw rate and lateral velocity under the condition of highway

Fig.12 Steering angle of front wheel under the condition of highway

Fig.13 Highway condition program runtime

Fig.14 Motion control results under double line change condition

Fig.15 Tracking deviation under double line change condition

Fig.16 Yaw rate and lateral velocity under double line change condition

Fig.17 Steering angle of front wheel under double line change condition

Fig.18 Program runtime under double line change condition

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