基于线性时变模型预测控制的实时抗噪高速车辆运动控制

doi:10.12382/bgxb.2023.0979

摘要/Abstract

摘要：

针对高速自动驾驶车辆实时高精度的运动控制问题,提出一种上层为基于路径点Cost的路径点筛选器与基于横纵向轮胎力分析的速度规划器、下层为基于线性时变动力学模型预测的路径跟踪控制器与速度控制器的两层架构,并引入最小均方(Least Mean Square, LMS)自适应状态估计器提升系统的抗噪性。路径点筛选器提升运算速度并减少筛选过程中的关键信息损失,速度规划器在安全行驶前提下生成最优速度曲线。路径跟踪控制器考虑跟踪偏差软约束,提升跟踪效果。LMS状态估计器基于在线矫正的动力学模型,对横摆角速度与横向速度在线估计。搭建dSPACE-TX2硬件在环仿真环境,在高速公路工况及双移线工况下对比所提出方案与传统运动跟踪控制。半实物仿真结果表明,所提出的运动控制架构提升了抗噪性能与21%的跟踪精度,且满足50Hz高频控制的要求。

关键词: 线性时变模型, 模型预测控制, 实时运动控制, 路径跟踪, 二次规划

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

中图分类号:

U469.79

任宏斌, 孙纪禹, 陈志铿, 赵玉壮, 杨林. 基于线性时变模型预测控制的实时抗噪高速车辆运动控制[J]. 兵工学报, 2024, 45(12): 4311-4322.

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.

图/表 22

图1 单轨车辆模型

Fig.1 Single-track vehicle model

图2 双层架构车辆运动控制系统

Fig.2 Two-layer architecture vehicle motion control system

表1 路况风险分级

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

图3 加速度-曲率-车速关系

Fig.3 Acceleration-curvature-speed relation

图4 LMS状态估计器算法框架

Fig.4 LMS state estimator algorithm framework

图5 硬件在环仿真环境

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

表2 仿真车辆参数

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

表3 仿真MPC参数

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

表3 仿真MPC参数

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

图6 高速公路工况曲率

Fig.6 Highway curvature

表4 路径点筛选器与车速规划器参数

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

图7 LMS状态估计器处理前后状态量

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

图8 有无LMS状态估计器跟踪偏差

Fig.8 Tracking deviation with and without LMS state estimator

图9 高速公路工况运动控制结果

Fig.9 Motion control results under the condition of highway

图10 高速公路工况跟踪偏差

Fig.10 Tracking deviation under the condition of highway

图11 高速公路工况横摆角速度、横向速度

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

图12 高速公路工况前轮转角

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

图13 高速公路工况程序运行时间

Fig.13 Highway condition program runtime

图14 双移线工况运动控制结果

Fig.14 Motion control results under double line change condition

图15 双移线工况跟踪偏差

Fig.15 Tracking deviation under double line change condition

图16 双移线工况横摆角速度、横向速度

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

图17 双移线工况前轮转角

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

图18 双移线工况程序运行时间

Fig.18 Program runtime under double line change condition

参考文献 21

[1]	ZHAO B, WANG H, LI Q, et al. PID Trajectory tracking control of autonomous ground vehicle based on genetic algorithm[C]// Proceedings of 2019 Chinese Control and Decision Conference. Nanchang, China: IEEE, 2019: 3677-3682.
[2]	AL-MAYYAHI A, WANG W, BIRCH P. Path tracking of autonomous ground vehicle based on fractional order PID controller optimized by PSO[C]// Proceedings of 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics. Herl'any, Slovakia: IEEE, 2015: 109-114.
[3]	高琳琳, 唐风敏, 郭蓬, 等. 自动驾驶横向运动控制的改进LQR方法研究[J]. 机械科学与技术, 2021, 40(3): 435-441.
	GAO L L, TANG F M, GUO P, et al. Research on improved LQR control for self-driving vehicle lateral motion[J]. Mechanical Science and Technology for Aerospace Engineering, 2021, 40(3): 435-441. (in Chinese)
[4]	胡杰, 钟鑫凯, 陈瑞楠, 等. 基于模糊LQR的智能汽车路径跟踪控制[J]. 汽车工程, 2022, 44(1): 17-25,43.
	HU J, ZHONG X K, CHEN R N, et al. Path tracking control of intelligent vehicles based on fuzzy LQR[J]. Automotive Engineering, 2022, 44(1): 17-25,43. (in Chinese)
[5]	WANG H, CHEN X, CHEN Y, et al. Trajectory tracking and speed control of cleaning vehicle based on improved pure pursuit algorithm[C]// Proceedings of 2019 Chinese Control Conference. Guangzhou, China: IEEE, 2019: 4348-4353.
[6]	WANG L, ZHAI Z Q, ZHU Z X, et al. Path tracking control of an autonomous tractor using improved stanley controller optimized with multiple-population genetic algorithm[J]. Actuators, 2022, 11(1): 22.
[7]	YAKUB F, MORI Y. Comparative study of autonomous path-following vehicle control via model predictive control and linear quadratic control[J]. Proceedings of the institution of Mechanical Engineers, Part D: Journal of automobile engineering, 2015, 229(12): 1695-1714.
[8]	YIN C, XU B, CHEN X L, et al. Nonlinear model predictive control for path tracking using discrete previewed points[C]// Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems. Rhodes, Greece: IEEE, 2020: 1-6.
[9]	ZHU G Z, JIE H, HONG W R. Nonlinear model predictive path tracking control for autonomous vehicles based on orthogonal collocation method[J]. International Journal of Control, Automation and Systems, 2023, 21(1): 257-270.
[10]	SHI Z Q, CHEN H, YU S Y, et al. Nonlinear model predictive control for autonomous vehicle drifting[J/OL]. International Journal of Robust & Nonlinear Control, 2023 (2023-03-30) [2023-09-28]. https://doi.org/10.1002/rnc.66 97.
[11]	邓海鹏, 麻斌, 赵海光, 等. 自主驾驶车辆紧急避障的路径规划与轨迹跟踪控制[J]. 兵工学报, 2020, 41(3): 585-594. doi: 10.3969/j.issn.1000-1093.2020.03.020
	DENG H P, MA B, ZHAO H G, et al. Path planning and tracking control of autonomous vehicle for obstacle avoidance[J]. Acta Armamentarii, 2020, 41(3): 585-594. (in Chinese) doi: 10.3969/j.issn.1000-1093.2020.03.020
[12]	ZHAO Y, GE L H, ZHONG S R, et al. An efficient solver of predictive control methods for motion control of autonomous vehicles with experimental verification[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2023, 237(6): 1423-1434.
[13]	CISNEROS P S G, VOSS S, WERNER H. Efficient nonlinear model predictive control via quasi-LPV representation[C]// Proceedings of the 2016 IEEE 55th Conference on Decision and Control. Las Vegas, NV, US: IEEE, 2016: 3216-3221.
[14]	ROSOLIA U, BORRELLI F. Learning how to autonomously race a car: a predictive control approach[J]. IEEE Transactions on Control Systems Technology, 2019, 28(6): 2713-2719.
[15]	KABZAN J, HEWING L, LINIGER A, et al. Learning-based model predictive control for autonomous racing[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3363-3370. doi: 10.1109/LRA.2019.2926677
[16]	ROSOLIA U, BORRELLI F. Learning model predictive control for iterative tasks. a data-driven control framework[J]. IEEE Transactions on Automatic Control, 2017, 63(7): 1883-1896.
[17]	ROSOLIA U, ZHANG X, BORRELLI F. Robust learning model-predictive control for linear systems performing iterative tasks[J]. IEEE Transactions on Automatic Control, 2021, 67(2): 856-869.
[18]	卢佳兴, 刘海鸥, 关海杰, 等. 基于双参数自适应优化的无人履带车辆轨迹跟踪控制[J]. 兵工学报, 2023, 44(4): 960-971.
	LU J X, LIU H O, GUAN H J, et al. Trajectory tracking control of unmanned tracked vehicles based on adaptive dual-parameter optimization[J]. Acta Armamentarii, 2023, 44(4): 960-971. (in Chinese) doi: 10.12382/bgxb.2022.0009
[19]	PACEJKA H B, BAKKER E. The magic formula tyre model[J]. Vehicle system dynamics, 1992, 21(S1): 1-18.
[20]	YASSINE K, NAIMA A O, VINCENT V, et al. Optimized self-adaptive PID speed control for autonomous vehicles[C]// Proceedings of the 26th International Conference on Automation and Computing. Portsmouth, UK: IEEE, 2021: 1-6.
[21]	MEYSAM G, GHAZAL M, REZA R, et al. Adaptive speed control of electric vehicles based on multi-agent fuzzy Q-learning[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 7(1): 102-110.