Integrated Control Method of Multi-axle Distributed Driving Unmanned Ground Vehicle in Handling Limit

doi:10.12382/bgxb.2023.0775

Abstract

Abstract:

Envelope control originated in the aerospace industry, which provides the safety guarantees and movement limit and brings a better performance in aircraft control. A vehicle dynamics controller, which pushes the vehicle in handling limit, is proposed based on 8×8 distributed driving unmanned ground vehicle and the core idea of envelope method. Firstly, a novel method is proposed to evaluate the driving force status of vehicle by establishing a tire slip circle. The tire slip status and “g-g” diagram are combined to achieve approaching the vehicle handling limit under autonomous driving, and to perform the vehicle’s mobility and maneuverability. On the other hand, the controller is used to insure the tracking accuracy during trajectory tracking which can complete tasks more accurately and efficiently. Subsequently, in considering the impact of external environmental uncertainties on the stability of vehicles in extreme states, the stability phase planes under different conditions are obtained based on the analysis of stability characteristics of vehicle lateral dynamics model, and the mathematical expressions of them are summarized according to change mechanism. A stability maintaining controller with yaw moment output is proposed by analyzing the stability phase plane. Finally, based on the output of the upper-level controller, a lower-level torque distribution control strategy is designed to achieve full performance through the optimal allocation of actuators. The integrated control strategy is deployed on an 8×8 prototype vehicle and was tested with multiple subjects under the condition of off-road. The test results show that the vehicle has better dynamic performance and safety in trajectory tracking under high-speed condition.

Key words: unmanned ground vehicle, multi-axle distributed driving, handling limit of vehicle, envelope control, trajectory tracking

CLC Number:

TP13

PAN Bo, LI Shengfei, WANG Yang, TAN Senqi, ZHANG Naisi, LUO Tian, CUI Xing. Integrated Control Method of Multi-axle Distributed Driving Unmanned Ground Vehicle in Handling Limit[J]. Acta Armamentarii, 2023, 44(11): 3279-3294.

Figures/Tables 17

References 33

[1]	ZHOU J Y, YU H. Safety critical control of mixed-autonomy traffic via a single autonomous vehicle[C]// Proceedings of IEEE Conference on Intelligent Transportation Systems. Macau, China: IEEE, 2022:3089-3094.
[2]	AMMOUR M, ORJUELA R, BASSET M. Collision avoidance for autonomous vehicle using MPC and time varying sigmoid safety constraints[J]. IFAC-PapersOnLine, 2021, 54(10): 39-44.
[3]	SEO J W, LEE J H, BAEK E, et al. Safety-critical control with nonaffine control inputs via a relaxed control barrier function for an autonomous vehicle[J]. IEEE Robotics & Automation Letters, 2022, 7(2): 1944-1951.
[4]	CHEN Y, CHEN S Z, REN H B, et al. Path tracking and handling stability control strategy with collision avoidance for the autonomous vehicle under extreme conditions[J]. IEEE Transaction on Vehicular Technology, 2022, 69(12): 14602-14617. doi: 10.1109/TVT.25 URL
[5]	WHITSON J A, GORSICH D, VANTSEVICH V V, et al. Military unmanned ground vehicle maneuver: a review and formulation[C] // Proceedings of SAE 2023 World Congress Experience. Detroit, MI, US: SAE International, 2023.
[6]	CHEN Y L, WANG J T, ZHU S T, et al. Knowledge graph construction for foreign military unmanned systems[J]. Communications in Computer and Information Science, 2022, 1711(6): 127-137.
[7]	WU S B, LI S H, GONG J W, et al. Modeling and quantitative evaluation method of environmental complexity for measuring autonomous capabilities of military unmanned ground vehicles[J]. Unmanned Systems, 2023, 11(4): 367-382. doi: 10.1142/S2301385023500176 URL
[8]	NARANJ J E, JIMENEZ F, ANGUITA M, et al. Automation kit for dual-mode military unmanned ground vehicle for surveillance missions[J]. IEEE Intelligent Transportation Systems Magazine, 2023, 12(4): 125-137. doi: 10.1109/MITS.5117645 URL
[9]	ANAND A, CHERUKURI M, SAMANTA I S, et al. Unmanned integrated autonomous vehicle: swayambhu[C] // Proceedings of the 2nd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology. Bhubaneswar, India: IEEE, 2022.
[10]	PARKER M, QUINN B, BATES J, et al. Exploring cold regions autonomous operations[J]. Journal of Terramechanics, 2021, 96:159-165. doi: 10.1016/j.jterra.2021.03.003 URL
[11]	AHMADI K D, RASHIDI A J, MOGHRI A M. Design and simulation of autonomous military vehicle control system based on machine vision and ensemble movement approach[J]. Journal of Supercomput, 2022, 78:17309-17347. doi: 10.1007/s11227-022-04565-6
[12]	IAGNEMMA M B K. Special issue on the darpa grand challenge, Part 2[J]. Journal of Field Robotics, 2006, 23:661-692. doi: 10.1002/rob.v23:9 URL
[13]	HE X K, LIU Y L, LÜ C, et al. Emergency steering control of autonomous vehicle for collision avoidance and stabilization[J]. Vehicle System Dynamics, 2019, 57(8): 1163-1187. doi: 10.1080/00423114.2018.1537494 URL
[14]	FU T F, ZHOU H L, LIU Z Y. NMPC-based path tracking control strategy for autonomous vehicles with stable limit handling[J]. IEEE Transactions on Vehicular Technology, 2022, 71(12):12499-12510. doi: 10.1109/TVT.2022.3196315 URL
[15]	DALLAS J, THOMPSON M, GOH J Y M, et al. A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles:arXiv:2304.12263V1[R]. Ithaca,NY,US:Cornell University, 2023:2304.12263V1.
[16]	HOU X H, ZHANG J Z, HE C K, et al. Autonomous driving at the handling limit using residual reinforcement learning[J]. Advanced Engineering Informatics, 2022, 54.DOI:10.1016/j.aei.2022.101754.
[17]	PAPINI G P R, PLEBE A, LIO M D, et al. A reinforcement learning approach for enacting cautious behaviours in autonomous driving system: Safe speed choice in the interaction with distracted pedestrians[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7):8805-8822. doi: 10.1109/TITS.2021.3086397 URL
[18]	BETZ J, ZHENG H, LINIGER A, et al. Autonomous vehicles on the edge: a survey on autonomous vehicle racing[J]. IEEE Open Journal of Intelligent Transportation Systems, 2022, 3: 458-488. doi: 10.1109/OJITS.2022.3181510 URL
[19]	ROSOLIA U, CARVALHO A, BORRELLI F. Autonomous racing using learning model predictive control[C]// Proceedings of IEEE American Control Conference. Seattle, WA, US: IEEE, 2017, 54: 5115-5120.
[20]	ROSOLIA U, ZHANG X, BORRELLI F. Robust learning model predictive control for iterative tasks: learning from experience[C]// Proceedings of the IEEE 56th Annual Conference on Decision and Control. Melbourne, VIC, Australia: IEEE, 2017: 1157-1162.
[21]	ROSOLIA U, BORRELLI F. Learning how to autonomously race a car: a predictive control approach[J]. IEEE Transactions on Control Systems Technology, 2022, 28(6): 2713-2719. doi: 10.1109/TCST.87 URL
[22]	胡宇辉, 王旭, 胡家铭, 等. 越野环境下无人驾驶车辆技术研究综述[J]. 北京理工大学学报, 2021, 41(11): 1137-1144.
	HU Y H, WANG X, HU J M, et al. An overview on unmanned vehicle technology in off-road environment[J]. Transactions of Beijing Institute of Technology, 2021, 41(11): 1137-1144. (in Chinese)
[23]	刘忠泽, 陈慧岩, 崔星, 等. 无人平台越野环境下同步定位与地图创建[J]. 兵工学报, 2019, 40(12): 2399-2406. doi: 10.3969/j.issn.1000-1093.2019.12.002
	LIU Z Z, CHEN H Y, CUI X, et al. Real-time lidar SLAM in off-road environment for UGV[J]. Acta Armamentarii, 2019, 40(12): 2399-2406. (in Chinese) doi: 10.3969/j.issn.1000-1093.2019.12.002
[24]	GRAF U, BORGES P, HERNANDEZ E, et al. Optimization-based terrain analysis and path planning in unstructured environments[C]// Proceedings of 2019 International Conference on Robotics and Automation. Montreal, Canada: IEEE, 2019: 5614-5620.
[25]	YU S Y, SHEN C K, ERSAL T. Nonlinear model predictive planning and control for high-speed autonomous vehicles on 3D terrains[J]. IFAC-PapersOnLine, 2021, 54(20): 412-417.
[26]	FAN J J, LIU Y Z, LIU Y. Motion control of tracked vehicles based on MRAC[C]// Proceedings of CICTP: Transportation Evolution Impacting Future Mobility-Selected Papers From the 20th COTA International Conference of Transportation Professionals. Xi’an, China: ASCE, 2020: 609-619.
[27]	胡家铭, 胡宇辉, 陈慧岩, 等. 基于模型预测控制的无人驾驶履带车辆轨迹跟踪方法研究[J]. 兵工学报, 2019, 40(3): 11-18.
	HU J M, HU Y H, CHEN H Y, et al. Research on trajectory tracking of unmanned tracked vehicles based on model predictive control[J]. Acta Armamentraii, 2019, 40(3):11-18. (in Chinese)
[28]	ZHAO Z Y, LIU H O, CHEN H Y, et al. Kinematics-aware model predictive control for autonomous high-speed tracked vehicles under the off-road conditions[J]. Mechanical System and Signal Processing, 2019, 123: 333-350. doi: 10.1016/j.ymssp.2019.01.005 URL
[29]	付苗苗. 高速无人车辆极限包络特性与轨迹跟踪方法研究[D]. 北京: 北京理工大学, 2016.
	FU M M. Research on envelope of high-speed unmanned vehicle and path tracking[D]. Beijing: Beijing Institute of Technology, 2016. (in Chinese)
[30]	解云鹏. 极限工况下无人驾驶车辆运动控制策略研究[D]. 镇江: 江苏大学, 2021.
	XIE Y P. Research on motion control strategy for extreme maneuvers of self-driving vehicle[D]. Zhenjiang: Jiangsu University, 2021. (in Chinese)
[31]	WANG Y, ZHAO X J, LI S F, et al. Path tracking of eight in-wheel-driving autonomous vehicle: controller design and experimental results[C] // Proceedings of IEEE ICUS. Beijing, China: IEEE, 2019: 672-677.
[32]	TU X Y, GAI J Y, TANG L. Robust navigation control of a 4WD/4WS agricultural robotic vehicle[J]. Computers and Electronics in Agriculture, 2019, 164:104829.
[33]	刘明春. 8×8轮毂电机驱动车辆操纵稳定性分析与控制研究[D]. 北京: 北京理工大学, 2015.
	LIU C M. Research on handling stability and control with 8×8 hub-motor driving vehicle[D]. Beijing: Beijing Institute of Technology, 2015. (in Chinese)

参数	含义
F_x_w_ij,F_y_w_ij,F_z_w_ij	轮胎坐标系下每个轮胎的纵、横、垂向力
F_xij,F_yij	大地坐标系下每个轮胎的纵、横向力
v_x, v_y, v_z, ω_x, ω_y, ω_z	车辆质心x轴、y轴、z轴方向的速度与角速度
m,a_x, a_y, m_c, l₁, l₂	整车质量、车辆纵向加速度、车辆横向加速度、车身质量、侧倾中心与质心之间高度、俯仰中心与质心之间高度
F_s_ij, F_ss_ij, F_sd_ij	悬架力、静态悬架力、动态悬架力
δ_ij, B, L_i	转向角、轮距、车轴与质心之间距离
I_x, I_y, I_z	车身x轴、y轴、z轴方向的转动惯量
β, θ, ϕ	车身质心的侧偏角、侧倾角、横摆角
α_s, C_D, A, ρ, f	路面坡度、风阻系数、迎风面积、空气密度、轮胎滚动阻力系数
J_e, T_ij, R_e, ω_ij	滚动力矩、驱动力矩、轮胎半径、轮胎转速
z_s, z_w	悬架变形量(位移)、轮胎垂向位移

参数	含义
F_x_w_ij,F_y_w_ij,F_z_w_ij	轮胎坐标系下每个轮胎的纵、横、垂向力
F_xij,F_yij	大地坐标系下每个轮胎的纵、横向力
v_x, v_y, v_z, ω_x, ω_y, ω_z	车辆质心x轴、y轴、z轴方向的速度与角速度
m,a_x, a_y, m_c, l₁, l₂	整车质量、车辆纵向加速度、车辆横向加速度、车身质量、侧倾中心与质心之间高度、俯仰中心与质心之间高度
F_s_ij, F_ss_ij, F_sd_ij	悬架力、静态悬架力、动态悬架力
δ_ij, B, L_i	转向角、轮距、车轴与质心之间距离
I_x, I_y, I_z	车身x轴、y轴、z轴方向的转动惯量
β, θ, ϕ	车身质心的侧偏角、侧倾角、横摆角
α_s, C_D, A, ρ, f	路面坡度、风阻系数、迎风面积、空气密度、轮胎滚动阻力系数
J_e, T_ij, R_e, ω_ij	滚动力矩、驱动力矩、轮胎半径、轮胎转速
z_s, z_w	悬架变形量(位移)、轮胎垂向位移

参数	含义	数值
C_D	整车空气阻力系数	0.45
f	轮胎地面阻力系数	0.4
k_u	纵向速度误差反馈系数	4000
k_κ	纵向滑移误差反馈系数	6150
k_α	纵向侧偏误差反馈系数	4230
k_no-slip	纵向非滑移误差反馈系数	2500
k_e	横向误差系数	1390
k_Δ_ψ	航向误差系数	545
K_qd	稳定性反馈系数	1320
q	稳定性权重矩阵系数	0.3
r_fd	稳定性反馈控制权重系数	0.355

参数	含义	数值
C_D	整车空气阻力系数	0.45
f	轮胎地面阻力系数	0.4
k_u	纵向速度误差反馈系数	4000
k_κ	纵向滑移误差反馈系数	6150
k_α	纵向侧偏误差反馈系数	4230
k_no-slip	纵向非滑移误差反馈系数	2500
k_e	横向误差系数	1390
k_Δ_ψ	航向误差系数	545
K_qd	稳定性反馈系数	1320
q	稳定性权重矩阵系数	0.3
r_fd	稳定性反馈控制权重系数	0.355