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含网络攻击的智能网联汽车路径跟踪状态估计与控制OACSTPCD

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

中文摘要英文摘要

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.

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.

易星;曹青松

江西科技学院 协同创新中心,南昌 330098江西科技学院 人工智能学院,南昌 330098

交通运输

智能网联汽车网络攻击路径跟踪状态估计信息物理系统

intelligent connected vehiclenetwork attackpath followingstate estimationCPS

《机械科学与技术》 2024 (001)

面向车载网络的电动汽车主动悬架鲁棒控制策略研究

159-165 / 7

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)及江西省教育厅科技项目(GJJ212007)

10.13433/j.cnki.1003-8728.20220205

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