Research on dynamic modeling of port autonomous driving truck

doi:10.3969/j.issn.1674-8484.2024.05.016

Abstract

Abstract:

A subsystem-coupled tractor-trailer articulation dynamic modeling method for autonomous driving trucks was proposed to meet the high-precision tractor-trailer articulation dynamic modeling requirements for autonomous driving simulation testing. Firstly, based on the tractor-trailer articulation relationship, an accurate kinematic description of trucks was carried out. According to the kinematic relationship between the tractor and trailer of autonomous driving trucks, the lateral, longitudinal, and yaw dynamics models of the tractor and trailer were established using Newtonian mechanics. Considering the control requirements of autonomous driving simulation testing, the drive, brake, tire, steering, and aerodynamic subsystems were described separately. Secondly, considering the variability of the loading quality of trucks, the position of the center of mass and the moment of inertia of the truck were calculated, and the vertical load of the tire was obtained based on assumptions, completing the construction of the coupled dynamics model of the entire subsystem. And numerical simulations were conducted under emergency braking and double shift line conditions. The accuracy was compared with TruckSim. Aiming at the port environment, a virtual simulation system was established based on the truck dynamics model. The results show that the RMSE is below 0.05 when the accuracy was compared with TruckSim; Under the port environment, the maximum deviation in path tracking testing was less than 0.6 m, indicating that this method can accurately describe the dynamic response of the container truck under different operating conditions.

Key words: autonomous driving, container truck, port, kinematics, dynamics

CLC Number:

U469.5

XIE Zhen, ZHOU Guofeng, WU Mingyu, CAO Shouqi. Research on dynamic modeling of port autonomous driving truck[J]. Journal of Automotive Safety and Energy, 2024, 15(5): 783-794.

Figures/Tables 21

References 24

[1]	李霖, 奚美丽. 港口自动驾驶集装箱卡车开发可行性分析[J]. 汽车与配件, 2021(10): 64-68.
	LI Lin, XI Meili. Feasibility analysis of port autonomous container truck development[J]. Autom Parts, 2021(10): 64-68. (in Chinese)
[2]	范厚明, 郭振峰, 杨宇. 预约机制下送箱集卡多集装箱码头调度问题[J]. 系统仿真学报, 2017, 29(12): 3051-3060.
	FAN Houming, GUO Zhenfeng, YANG Yu. Truck scheduling for delivering containers among multiple container terminals based on the truck appointment system[J]. J Syst Simul, 2017, 29(12): 3051-3060. (in Chinese)
[3]	和福建, 马文博, 田晓笛. 港口自动驾驶现状及典型场景测试技术研究[J]. 汽车电器, 2022(8): 1-2+5.
	HE Fujian, MA Wenbo, TIAN Xiaodi. Research on the status of port autonomous driving and testing technology of typical scenes[J]. Auto Electric Parts, 2022(8): 1-2+5. (in Chinese)
[4]	Lima P F. Optimization-based motion planning and model predictive control for autonomous driving with experimental evaluation on a heavy-duty construction truck[D]. Stockholm, Sweden: KTH Royal Institute of Technology, 2018.
[5]	David J, Valencia R, Philippsen R, et al. Gradient based path optimization method for autonomous driving[C]// 2017 IEEE/RSJ Int’l Conf Intel Robo Sys (IROS), Vancouver, BC, Canada, 2017.
[6]	TANG Luqi, YAN Fuwu, ZOU Bin, et al. An improved kinematic model predictive control for high-speed path tracking of autonomous vehicles[J]. IEEE Access, 2020, 8: 51400-51413.
[7]	HE Zhengyi, JI Xuewu. Nonlinear robust control of integrated vehicle dynamics[J]. Vehi Syst Dyna, Int’l J Vehi Mech Mobil, 2012, 50: 247-280.
[8]	QIU Runqi, XIN Shangfeng. Research on lateral control of autonomous vehicle based on driver steering model[J]. J Phys: Conf Seri, 2022, 2206(1): No 012021.
[9]	付豪. 无人驾驶铰接式扫地车运动规划的研究[D]. 合肥: 合肥工业大学, 2022.
	FU Hao. Research on motion planning of driverless articulated sweeper[D]. Hefei: Hefei University of Technology, 2022. (in Chinese)
[10]	FAN Minglei, YUE Ming, ZHANG Hongzhi, et al. Anti-jackknife reverse perpendicular parking control of tractor-trailer vehicle via MPC technique[C]// 2019 IEEE 9th Annu Int’l Conf CYBER Tech Auto, Control, Intel Syst (CYBER), Suzhou, China, 2019.
[11]	LEI Guannan, ZHENG Yili. Research on cooperative trajectory planning algorithm based on tractor-trailer wheeled robot[J]. IEEE Access, 2022, 10: 64209-64221.
[12]	RYU J-C, Agrawal S K, Franch J. Motion planning and control of a tractor with a steerable trailer using differential flatness[J]. J Comput Nonlin Dyna, 2008, 3(3): No 031003.
[13]	YU Minghui, GONG Xue, FAN Guowei, et al. Trajectory planning and tracking for carrier aircraft-tractor system based on autonomous and cooperative movement[J]. Mathe Prob Engi, 2020, 2020: e6531984.
[14]	Abroshan M, Taiebat M, Goodarzi A, et al. Automatic steering control in tractor semi-trailer vehicles for low-speed maneuverability enhancement[J]. Proc Institut Mech Engi, Part K: J Multi-Body Dyna, 2017, 231(1): 83-102.
[15]	赵树恩, 张雄, 唐俊涛. 重型半挂汽车列车防侧翻分层递阶控制[J]. 汽车安全与节能学报, 2019, 10(4): 474-482.
	ZHAO Shuen, ZHANG Xiong, TANG Juntao. Multilevel hierarchical control of anti-rollover for heavy-duty tractor-semitrailer[J]. J Autom Safe Energ Conserv, 2019, 10(4): 474-482. (in Chinese)
[16]	高路路. 铰接式无轨车辆路径跟踪与操纵稳定性集成控制研究[D]. 北京: 北京科技大学, 2021.
	GAO Lulu. Research on integrated control of path tracking and handling stability of articulated steering vehicles[D]. Beijing: University of Science and Technology Beijing, 2021. (in Chinese)
[17]	TUNG N. Setting up the braking force measurement system of the tractor semi-trailer[J]. Engi Solid Mech, 2021, 9(4): 415-424.
[18]	张超. 分布式驱动铰接车转向姿态控制策略研究[D]. 长春: 吉林大学, 2023.
	ZHANG Chao. Research on steering attitude control strategy of distributed drive articulated vehicle[D]. Changchun: Jilin University, 2023. (in Chinese)
[19]	杨拯, 曾小伟, 雷丁瑞, 等. 基于扩展卡尔曼滤波算法的港口智能无人内集卡状态估计研究[J]. 港口装卸, 2023(6): 23-26.
	YANG Zheng, ZENG Xiaowei, LEI Dingrui, et al. Research on state estimation of port intelligent unmanned internal truck based on extended kalman filter algorithm[J]. Port Operat, 2023(6): 23-26. (in Chinese)
[20]	王福泰. 飞机程控牵引车行走驱动控制技术研究[D]. 北京: 中国农业机械化科学研究院, 2023.
	WANG Futai. Research on travel drive control system technology of aircraft program-controlled tractor[D]. Beijing: China Agricultural Machinery Science and Research Institute, 2023. (in Chinese)
[21]	王常顺. 智能网联无人集装箱卡车轨迹跟踪与队列协同控制[D]. 大连: 大连海事大学, 2023.
	WANG Changshun. Trajectory tracking and cooperative platooning control of intelligent connected unmanned container transporter[D]. Dalian: Dalian Maritime University, 2023. (in Chinese)
[22]	Voropaev G D, Sidorov V N, Maksimovich K Y, et al. Influence of mass affecting tractor’s rear axle and rigidity of tires on the control coefficient[J]. IOP Conf Ser: Earth Environ Sci, 2021, 839(5): No 052047.
[23]	Sidorov M V, Troyanovskaya I P, Sokolova V A, et al. Investigation of the damping properties of the process module for a tractor of traction class 1.4[J]. IOP Conf Ser: Earth Environ Sci, 2021, 839(5): No 052056.
[24]	宋胜. 轮毂电机驱动车辆轨迹跟踪纵横向综合控制研究[D]. 重庆: 西南大学, 2024.
	SONG Sheng. Research on longitudinal and lateral coupling control for trajectory tracking of in-wheel motor driven vehicles[D]. Chongqing: Southwest University, 2024. (in Chinese)

m_s1	4.455 t	I_{zz1_s}	34.803 t·m²
m_u_s1	570 kg	I_{zz1_A1}	335 kg·m²
m_u_s2	785 kg	I_{zz1_A2}	305 kg·m²
m_u_s3	785 kg	I_{zz1_A3}	305 kg·m²
m_s2	5.5 t	I_{zz2_s}	150 t·m²
m_u_s4	690 kg	I_{zz2_A4}	265 kg·m²
m_u_s5	690 kg	I_{zz2_A5}	265 kg·m²
m_u_s6	690 kg	I_{zz2_A6}	265 kg·m²

m_s1	4.455 t	I_{zz1_s}	34.803 t·m²
m_u_s1	570 kg	I_{zz1_A1}	335 kg·m²
m_u_s2	785 kg	I_{zz1_A2}	305 kg·m²
m_u_s3	785 kg	I_{zz1_A3}	305 kg·m²
m_s2	5.5 t	I_{zz2_s}	150 t·m²
m_u_s4	690 kg	I_{zz2_A4}	265 kg·m²
m_u_s5	690 kg	I_{zz2_A5}	265 kg·m²
m_u_s6	690 kg	I_{zz2_A6}	265 kg·m²

参数	最大误差	RMSE
a_x₁ / (m·s^-2)	1.643 00	0.045 88
a_x₂ / (m·s^-2)	1.564 00	0.042 26
v_x₁ / (m·s^-1)	0.027 31	0.005 28
v_x₂ / (m·s^-1)	0.017 70	0.002 03

参数	最大误差	RMSE
a_x₁ / (m·s^-2)	1.643 00	0.045 88
a_x₂ / (m·s^-2)	1.564 00	0.042 26
v_x₁ / (m·s^-1)	0.027 31	0.005 28
v_x₂ / (m·s^-1)	0.017 70	0.002 03

参数	最大误差	RMSE
牵引车X方向位移/ m	0.034 51	0.013 20
牵引车Y方向位移/ m	0.008 87	0.004 22
挂车X方向位移/ m	0.041 57	0.012 89
挂车Y方向位移/ m	0.012 23	0.003 84
a_x₁ / (m·s^-2)	0.089 71	0.006 60
a_y₁ / (m·s^-2)	0.096 10	0.044 36
a_x₂ / (m·s^-2)	0.041 67	0.012 89
a_y₂ / (m·s^-2)	0.042 02	0.020 62
牵引车横摆角加速度 / (rad·s^-2)	0.012 60	0.004 89
挂车横摆角加速度 / (rad·s^-2)	0.002 77	0.001 14