执行器攻击下自主无人车辆模糊滑模安全控制

doi:10.3969/j.issn.1674-8484.2023.06.010

汽车安全与节能学报 ›› 2023, Vol. 14 ›› Issue (6): 734-743.DOI: 10.3969/j.issn.1674-8484.2023.06.010

执行器攻击下自主无人车辆模糊滑模安全控制

孙洪涛¹(), 王晨¹, 赵庆堂², 王志文³

1.曲阜师范大学工学院，日照 276826，中国
2.潍柴雷沃智慧农业科技股份有限公司，潍坊 261200，中国
3.兰州理工大学电气工程与信息工程学院，兰州 730050，中国

收稿日期:2023-06-29 修回日期:2023-08-30 出版日期:2023-12-31 发布日期:2023-12-29
作者简介:孙洪涛（1987—），男（汉），山东，副教授。E-mail：huntsun@qfnu.edu.cn。
基金资助:
国家自然科学基金项目(62103229);国家自然科学基金项目(62263019);山东省自然科学基金项目(ZR2021QF026);中国博士后科学基金项目(2021M692024);教育部产学合作协同育人项目(220700595203354)

Fuzzy sliding mode security control of autonomous unmanned vehicle actuator attacks

SUN Hongtao¹(), WANG Chen¹, ZHAO Qingtang², WANG Zhiwen³

1. College of Engineering, Qufu Normal University, Rizhao 276826, China
2. Weichai Lovol Intelligent Agricultural Technology Co., Ltd, Weifang, 261200, China
3. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Received:2023-06-29 Revised:2023-08-30 Online:2023-12-31 Published:2023-12-29

摘要/Abstract

摘要：

针对发生执行器攻击现象的自主无人车辆（AUV）路径跟踪控制问题，提出一种基于模糊滑模变结构的鲁棒安全控制设计方法。根据AUV的动力学行为建立了基于状态空间的路径跟踪控制模型，利用滑动模态对执行器攻击信号的不变性，设计了一种积分型滑模安全控制策略，通过选取合适的Lyapunov函数，推导出发生执行器攻击情形下AUV路径跟踪控制的稳定性和前轮转角控制增益。进一步，通过结合模糊控制规则设计了可自适应调节的切换项增益，利用MATLAB/Simulink和CarSim联合仿真实验平台验证所提控制策略的有效性。结果表明：相较于执行器攻击下，车速为72 km/h，在单移线和双移线2种工况下，路径跟踪最大误差分别减少5.6%、6.2%，方向盘转角减少73.9%、75.4%，质心侧偏角减少249.9%、213.9%。该控制策略能够增加AUV路径跟踪过程中的鲁棒性，保证良好的路径跟踪性能。

关键词: 自主无人车辆（AUV）, 路径跟踪, 执行器攻击, 滑模控制, 模糊控制

Abstract:

A robust safety control design method based on fuzzy sliding mode variable structure was proposed to solve the path following control problem of autonomous unmanned vehicle (AUV) with actuator attacks. A state-space-based path following control model was established to characterize the dynamic behaviors of AUV. Using the invariance of sliding mode to the actuator attack signal, an integral sliding mode safety control strategy was designed, by selecting appropriate Lyapunov function candidate, the stability of AUV path following control and the gain of front wheel angle control under actuator attacks were derived. The adaptively adjustable switching term gain was designed by combining fuzzy control rules, MATLAB/Simulink and CarSim co-simulation platform were used to verify the effectiveness of the proposed control strategy. The results show that the maximum error of path following is reduced by 5.6% and 6.2%, the steering wheel angle is reduced by 73.9% and 75.4%, and the side slip is reduced by 249.9% and 213.9%, respectively comparing with the case that the vehicle speed is 72 km/h under single and double line-shifting conditions. This control strategy can increase the robustness of AUV path following process and ensure good path following performance.

Key words: autonomous unmanned vehicle(AUV), path following, actuator attacks, sliding mode control, fuzzy control

中图分类号:

TP273

孙洪涛, 王晨, 赵庆堂, 王志文. 执行器攻击下自主无人车辆模糊滑模安全控制[J]. 汽车安全与节能学报, 2023, 14(6): 734-743.

SUN Hongtao, WANG Chen, ZHAO Qingtang, WANG Zhiwen. Fuzzy sliding mode security control of autonomous unmanned vehicle actuator attacks[J]. Journal of Automotive Safety and Energy, 2023, 14(6): 734-743.

图/表 11

图1 二自由度车辆动力学模型

图2 执行器攻击下AUV路径跟踪控制架构

图3 模糊输入/输出对应的隶属度函数

s(t)?(t)	NB	NM	NS	ZO	PS	PM	PB
к(t)	NB	NM	NS	ZO	PS	PM	PB

表1 模糊规则设计表

NB	NM	NS	ZO
[-2 -1.35)	[-1.35 -0.668)	[-0.668 0)	0
PS	PM	PB
(0 0.668]	(0.668 1.35]	(1.35 2]

表2 模糊阈值界定规则表

图4 MATLAB/Simulink和CarSim联合仿真框图

整车质量，m	1.270 t
纵向车速，v_x	72 km / ·h
质心到前轴距离，a	1.015 m
质心到后轴距离，b	1.895 m
前轮侧偏刚度，C_f	-110.300 kN / rad
后轮侧偏刚度，C_r	-84.988 kN / rad
方向盘转角传动比	17.6
转动惯量，I_z	1536.7 km·m²

表3 车辆仿真参数

图5 执行器攻击下AUV单移线路径跟踪仿真结果

图6 单移线工况下模糊滑模控制与滑模控制效果

图7 执行器攻击下AUV双移线路径跟踪仿真结果对比

图8 双移线工况下模糊滑模控制与滑模控制效果对比

参考文献 20

[1]	胡笳, 罗书源, 赖金涛, 等. 自动驾驶对交通运输系统规划的影响综述[J]. 交通运输系统工程与信息, 2021, 21(5): 52-65+76.
	HU Jia, LUO Shuyuan, LAI Jintao, et al. A Review of the impact of autonomous driving on transportation planning[J]. J Transport Syst Engi Info Tech, 2021, 21(5): 52-65+76. (in Chinese)
[2]	SUN Hongtao, PENG Chen. Event-triggered adaptive security path following control for unmanned ground vehicles under sensor attacks[J/OL], IEEE Trans Vehi Tech, 2023, 72(7):8500-8509.
[3]	YANG Fan, GU Zhou, HUA Lingzhi, et al. A resource-aware control approach to vehicle platoons under false data injection attacks[J]. ISA Trans, 2022, 131: 367-376. doi: 10.1016/j.isatra.2022.04.046 URL
[4]	易星, 曹青松. 含网络攻击的智能网联汽车路径跟踪状态估计与控制[J/OL]. 机械科学与技术. [2023-05-14]. https://doi.org/10.13433/j.cnki.1003-8728.20220205.
	YI Xing, CAO Qingsong. State estimation and control for path following of intelligent connected vehicle with network attack[J/OL]. Mech Sci Tech Aero Engi. [2023-05-14]. https://doi.org/10.13433/j.cnki.1003-8728.20220205. (in Chinese)
[5]	SUN Hongtao, PENG Chen, DING Fei. Self-discipline predictive control of autonomous vehicles against denial of service attacks[J]. Asia J Contr, 2022, 24(6): 3538-3551.
[6]	Biroon R A, Pisu P, Abdollahi Z. Real-time false data injection attack detection in connected vehicle systems with PDE modeling[C]// Ame Contr Conf, Denver, CO, USA, 2020: 3267-3272.
[7]	LI Xiaomeng, ZHOU Qi, LI Panshou, et al. Event-triggered consensus control for multi-agent systems against false data-injection attacks[J]. IEEE Trans Cybernet, 2019, 50(5): 1856-1866. doi: 10.1109/TCYB.6221036 URL
[8]	SUN Qi, SHI Yang. Model predictive control as a secure service for cyber-physical systems: a cloud-edge framework[J]. IEEE Intenet Things J, 2021, 9(22): 22194-22203.
[9]	ZHA Lijuan, LIAO Rongfei, LIU Jinliang, et al. Dynamic event-triggered output feedback control for networked systems subject to multiple cyber attacks[J]. IEEE Trans Cybernet, 2021, 52(12): 13800-13808. doi: 10.1109/TCYB.2021.3125851 URL
[10]	SUN Hongtao, ZHANG Pengfei, PENG Chen. Output-sensitive event-triggered path following control of autonomous ground vehicles under stochastic FDI attacks[J]. J Franklin Instit, 2023, 360(3): 2307-2325. doi: 10.1016/j.jfranklin.2022.10.046 URL
[11]	张新荣, 许权宁, 宫新乐, 等. 人机共驾车辆路径跟踪集成控制策略[J]. 汽车安全与节能学报, 2022, 13(4): 686-696.
	ZHANG Xinrong, XU Quanning, GONG Xinle, et al. Integrated control strategy for path tracking of human machineco-driving vehicle[J]. J Autom Safe Energ, 2022, 13(4): 686-696. (in Chinese)
[12]	XU Shaobing, PENG Huei. Design, analysis, and experiments of preview path tracking control for autonomous vehicles[J]. IEEE Trans Intel Transport Syst, 2019, 21(1): 48-58. doi: 10.1109/TITS.6979 URL
[13]	李耀华, 刘洋, 冯乾隆, 等. 基于最优预瞄和模型预测的智能商用车路径跟踪控制[J]. 汽车安全与节能学报, 2020, 11(4): 462-469.
	LI Yaohua, LIU Yang, FENG Qianlong, et al. Path tracking control for an intelligent commercial vehicle based on optimal preview and model predictive[J]. J Autom Safe Energ, 2020, 11(4): 462-469. (in Chinese)
[14]	LUAN Zhongkai, ZHANG Jinning, ZHAO Wanzhong, et al. Trajectory tracking control of autonomous vehicle with random network delay[J]. IEEE Trans Vehi Tech, 2020, 69(8): 8140-8150.
[15]	JIN Xu, Haddad W M, JIANG Zhongping, et al. Adaptive control for mitigating sensor and actuator attacks in connected autonomous vehicle platoons[C]// IEEE Conf Deci Contr. IEEE, 2018: 2810-2815.
[16]	李磊, 李军, 张世义. 搭载改进滑模控制的自动驾驶汽车轨迹跟踪控制[J]. 汽车安全与节能学报, 2020, 11(4): 503-510.
	LI Lei, LI Jun, ZHANG Shiyi. Trajectory tracking control of autonomous vehicles with optimized sliding mode control[J]. J Autom Safe Energ, 2020, 11(4): 503-510. (in Chinese)
[17]	XU Yangguang, GUO Ge, YU Shuanghe. Resilient observer-based sliding mode control of connected vehicles with denial-of-service attacks[J]. J Franklin Instit, 2022, 359(7): 2886-2905. doi: 10.1016/j.jfranklin.2022.02.036 URL
[18]	JIN Xu, Haddad W M, JIANG Zhongping, et al. An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons[J]. Int’l J Adapt Contr Sign Process, 2019, 33(12): 1788-1802.
[19]	周星, 刘夫云, 唐振天, 等. 基于轨迹预测最优侧向加速度的驾驶员模型[J]. 汽车安全与节能学报, 2023, 14(3): 338-345.
	ZHOU Xing, LIU Fuyun, TANG Zhentian, et al. Optimal lateral acceleration driver model based on trajectory prediction[J]. J Autom Safe Energ, 2023, 14(3): 338-345. (in Chinese)
[20]	郭孔辉. 驾驶员—汽车闭环系统操纵运动的预瞄最优曲率模型[J]. 汽车工程, 1984 (3): 1-16.
	GUO Konghui. Drivers-vehicle close-loop simulation of handling by “Preselect optimal curvature method”[J]. Autom Engineering, 1984 (3): 1-16. (in Chinese)

执行器攻击下自主无人车辆模糊滑模安全控制

Fuzzy sliding mode security control of autonomous unmanned vehicle actuator attacks

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[2]	张新荣, 许权宁, 宫新乐, 李学鋆, 黄晋. 人机共驾车辆路径跟踪集成控制策略[J]. 汽车安全与节能学报, 2022, 13(4): 686-696.
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[5]	李耀华, 范吉康, 刘洋, 何杰, 李泽田, 潘绍飞. 自适应双时域参数MPC的智能车辆路径规划与跟踪控制[J]. 汽车安全与节能学报, 2021, 12(4): 528-539.
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[8]	李磊, 李军, 张世义. 搭载改进滑模控制的自动驾驶汽车轨迹跟踪控制[J]. 汽车安全与节能学报, 2020, 11(4): 503-510.
[9]	裴晓飞，刘志厅，陈祯福，陈可际，杨波 . 分布式驱动电动汽车的差速转向控制及其适用性[J]. JASE, 2019, 10(4): 423-432.
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