含网络攻击的智能网联汽车路径跟踪状态估计与控制

易星; 曹青松

doi:10.13433/j.cnki.1003-8728.20220205

含网络攻击的智能网联汽车路径跟踪状态估计与控制

doi: 10.13433/j.cnki.1003-8728.20220205

易星^1,,
曹青松^2, ,

1.
江西科技学院协同创新中心, 南昌 330098
2.
江西科技学院人工智能学院, 南昌 330098

基金项目:

国家自然科学基金项目 51765021

江西省科技厅重点研发计划项目 20181BBE50012

江西省教育厅科技项目 GJJ212007

详细信息

作者简介:
易星, 讲师, 硕士, yxing9002@163.com

通讯作者:
曹青松, 副教授, 博士, 3524925913@qq.com

中图分类号: U461.1
计量
- 文章访问数: 77
- HTML全文浏览量: 32
- PDF下载量: 13
- 被引次数: 0
出版历程
- 收稿日期: 2021-11-20
- 刊出日期: 2024-01-25

State Estimation and Control for Path Following of Intelligent Connected Vehicle with Network Attack

YI Xing^1
,,
CAO Qingsong^{2
, ,}

1.
Collaborative Innovation Center, Jiangxi University of Technology, Nanchang 330098, China
2.
School of Artificial Intelligence, Jiangxi University of Technology, Nanchang 330098, China

摘要

摘要: 智能网联汽车具有信息物理系统(Cyber-physical systems, CPS)的特征，运行中容易受到网络攻击的不利影响，造成通信数据的异常交互，降低行车安全。建立智能网联汽车路径跟踪动力学模型和两自由度汽车操纵动力学模型，分析车辆路径跟踪控制系统信息架构，考虑系统响应存在网络攻击，将连续系统状态空间方程进行离散化处理。根据线性二次估计设计一种递归状态估计器。仿真研究网络攻击对智能网联汽车路径跟踪的影响，状态估计器对网络攻击下车辆路径跟踪控制的效果及鲁棒性。结果表明: 网络攻击会导致智能网联汽车路径跟踪效果变差，验证了状态估计器能够有效改善网络攻击对车辆跟踪控制的不利影响，且当网络攻击程度λ、协方差P初值的不同，状态估计器表现出较好的鲁棒性。本研究能够保证网络攻击下智能网联汽车数据信息的可靠交互，有利于改善智能网联汽车跟踪行驶的性能。
- 智能网联汽车 /
- 网络攻击 /
- 路径跟踪 /
- 状态估计 /
- 信息物理系统
Abstract: Intelligent connected vehicle has the characteristics of CPS, vulnerable to the adverse impact of network attack in operation, then resulting in abnormal communication data interaction, and reducing driving safety. The path following dynamics model of intelligent connected vehicle and 2-DOF vehicle handling dynamics model are established. The information architecture for path following control system of vehicles is analyzed. Considering that there is a network attack in the system response, the state space equation of continuous system was discretized. A recursive state estimator based on linear quadratic estimation is designed. The influence of network attack on path following of intelligent connected vehicle, and the effect and robustness of state estimator on control for path following of vehicle under network attack are simulated. The results show that network attack will make the path following effect of intelligent connected vehicle worse. The state estimator can effectively improve the negative influence of network attack on vehicle tracking control. The state estimator shows good robustness with the difference of network attack degree λ and initial value of covariance P. This study can ensure the reliable interaction of data and information for intelligent connected vehicle under network attack, which is beneficial to improve the tracking performance of intelligent connected vehicle.
- intelligent connected vehicle /
- network attack /
- path following /
- state estimation /
- CPS

HTML全文

图 1 车辆路径跟踪模型

Figure 1. Path following model of vehicle

下载: 全尺寸图片幻灯片

图 2 两自由度汽车操纵动力学模型

Figure 2. 2-DOF vehicle handling dynamics model

下载: 全尺寸图片幻灯片

图 3 网络攻击下智能网联汽车路径跟踪控制信息架构

Figure 3. Information architecture for path following control of intelligently connected vehicle under network attack

下载: 全尺寸图片幻灯片

图 4 车辆路径跟踪的目标路径

Figure 4. Target path of vehicle path following

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图 5 LQR控制下车辆路径跟踪的横向偏差曲线

Figure 5. Lateral deviation curve of vehicle path following under LQR control

下载: 全尺寸图片幻灯片

图 6 网络攻击下车辆路径跟踪的质心侧偏角曲线

Figure 6. Side-slip angle curve of vehicle path following under network attack

下载: 全尺寸图片幻灯片

图 7 不同网络攻击程度下车辆路径跟踪状态响应曲线

Figure 7. Response curve of vehicle path following state under different degrees of network attack

下载: 全尺寸图片幻灯片

图 8 不同协方差P初值下车辆路径跟踪状态响应曲线

Figure 8. Response curve of vehicle path following state under different initial value of covariance P

下载: 全尺寸图片幻灯片

表 1 汽车主要参数

Table 1. Main parameters of vehicle

参数	数值
车辆总质量m	1 285 kg
质心距前轴距离a	1.10 m
质心距后轴距离b	1.60 m
前轮侧偏刚度C_αf	61 500 N/rad
后轮侧偏刚度C_αr	61 500 N/rad
车辆绕z轴的转动惯量I	1 750 kg·m²

下载: 导出CSV

参考文献(16)

[1]	宗长富, 代昌华, 张东. 智能汽车的人机共驾技术研究现状和发展趋势[J]. 中国公路学报, 2021, 34(6): 214-237. ZONG C F, DAI C H, ZHANG D. Human-machine interaction technology of intelligent vehicles: current development trends and future directions[J]. China Journal of Highway and Transport, 2021, 34(6): 214-237. (in Chinese)
[2]	李克强. 智能网联汽车信息物理系统参考架构1.0发布[J]. 智能网联汽车, 2019(6): 66-69. LI K Q. The reference framework 1.0 for intelligent connected vehicle information physical system was released[J]. Intelligent Connected Vehicles, 2019(6): 66-69. (in Chinese)
[3]	YAO Q Q, TIAN Y, WANG Q, et al. Control strategies on path tracking for autonomous vehicle: state of the art and future challenges[J]. IEEE Access, 2020, 8: 161211-161222. doi: 10.1109/ACCESS.2020.3020075
[4]	杨飞生, 汪璟, 潘泉, 等. 网络攻击下信息物理融合电力系统的弹性事件触发控制[J]. 自动化学报, 2019, 45(1): 110-119. YANG F S, WANG J, PAN Q, et al. Resilient event-triggered control of grid cyber-physical systems against cyber attack[J]. Acta Automatica Sinica, 2019, 45(1): 110-119. (in Chinese)
[5]	周雪, 张皓, 王祝萍. 扩展卡尔曼滤波在受到恶意攻击系统中的状态估计[J]. 自动化学报, 2020, 46(1): 38-46. ZHOU X, ZHANG H, WANG Z P. Extended Kalman filtering in state estimation systems with malicious attacks[J]. Acta Automatica Sinica, 2020, 46(1): 38-46. (in Chinese)
[6]	任玥, 冀杰, 赵颖, 等. 基于最小模型误差估计的智能汽车路径跟踪控制[J]. 汽车工程, 2021, 43(4): 580-587. REN Y, JI J, ZHAO Y, et al. Path tracking control of intelligent vehicle based on minimal model error estimation[J]. Automotive Engineering, 2021, 43(4): 580-587. (in Chinese)
[7]	周卫琪, 齐翔, 陈龙, 等. 基于无迹卡尔曼滤波与遗传算法相结合的车辆状态估计[J]. 汽车工程, 2019, 41(2): 198-205. ZHOU W Q, QI X, CHEN L, et al. Vehicle state estimation based on the combination of unscented Kalman filtering and genetic algorithm[J]. Automotive Engineering, 2019, 41(2): 198-205. (in Chinese)
[8]	HU C, WANG Z F, TAGHAVIFAR H, et al. MME-EKF-based path-tracking control of autonomous vehicles considering input saturation[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5246-5259. doi: 10.1109/TVT.2019.2907696
[9]	李笑宇, 冯肖雪, 潘峰, 等. 网络攻击下无人机信息物理系统的自适应状态估计[J]. 航空学报, 2022, 43(3): 429-442. LI X Y, FENG X X, PAN F, et al. Adaptive state estimate for unmanned aircraft cyber-physical system with cyber attack[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(3): 429-442. (in Chinese)
[10]	张会霞. 基于博弈论的信息物理系统安全估计研究[D]. 秦皇岛: 燕山大学, 2019. ZHANG H X. Research on security estimation of cyber physics systems based on game theory[D]. Qinhuangdao: Yanshan University, 2019. (in Chinese)
[11]	敖伟, 宋永端, 温长云. 受攻击信息物理系统的分布式安全状态估计与控制——一种有限时间方法[J]. 自动化学报, 2019, 45(1): 174-184. AO W, SONG Y D, WEN C Y. Distributed secure state estimation and control for CPSs under sensor attacks——a finite time approach[J]. Acta Automatica Sinica, 2019, 45(1): 174-184. (in Chinese)
[12]	刘珊. 信息物理系统在网络攻击下的控制问题研究[D]. 广州: 华南理工大学, 2019. LIU S. Research on the control problems for cyber physical system under network attacks[D]. Guangzhou: South China University of Technology, 2019. (in Chinese)
[13]	LEE J G, KIM J, SHIM H. Fully distributed resilient state estimation based on distributed median solver[J]. IEEE Transactions on Automatic Control, 2020, 65(9): 3935-3942.
[14]	LU A Y, YANG G H. Secure state estimation for cyber-physical systems under sparse sensor attacks via a switched Luenberger observer[J]. Information Sciences, 2017, 417: 454-464.
[15]	张守武, 李擎, 王恒, 等. 非匹配不确定性影响下的无人车路径跟踪控制[J]. 控制与决策, 2022, 37(1): 160-166. ZHANG S W, LI Q, WANG H, et al. Path following control for autonomous vehicles with mismatched uncertainties[J]. Control and Decision, 2022, 37(1): 160-166. (in Chinese)
[16]	BEZZO N, WEIMER J, PAJIC M, et al. Attack resilient state estimation for autonomous robotic systems[C]//Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL, USA: IEEE, 2014: 3692-3698.