混合网络攻击下车辆队列的无模型自适应弹性控制

韩乔妮; 马建国; 李鹏; 左志强

doi:10.11999/JEIT251135

混合网络攻击下车辆队列的无模型自适应弹性控制

doi: 10.11999/JEIT251135 cstr: 32379.14.JEIT251135

1.
天津大学电气自动化与信息工程学院天津市 300072
2.
天津市智能无人集群技术与系统重点实验室天津市 300072

基金项目: 国家自然科学基金 (62473280, 62403350)，中国博士后科学基金资助项目 (2024M762356)

详细信息

作者简介:
韩乔妮：女，副教授，博士，主要研究方向为无线通信、边缘计算、分布式控制及应用

马建国：男，硕士生，主要研究方向为网联车辆队列、弹性控制、数据驱动控制

李鹏：男，助理研究员，博士，主要研究方向为模型预测控制、鲁棒控制、智能网联车辆

左志强：男，教授，博士，主要研究方向为模型预测控制、数据驱动控制、无人集群系统

通讯作者:
左志强　zqzuo@tju.edu.cn

中图分类号: TP273
计量
- 文章访问数: 24
- HTML全文浏览量: 7
- PDF下载量: 2
- 被引次数: 0
出版历程
- 修回日期: 2026-02-05
- 录用日期: 2026-02-05
- 网络出版日期: 2026-02-16

Model-Free Adaptive Resilient Control of Vehicle Platoons Against Hybrid Cyberattacks

1.
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China, China
2.
Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, Tianjin University, Tianjin 300072, China

Funds: The National Natural Science Foundation of China (62473280, 62403350), The China Postdoctoral Science Foundation (2024M762356)

摘要

摘要: 随着智能交通系统的快速发展，网联车辆队列作为其核心组成，在提升交通效率、行车安全性和能源利用率方面展现出巨大潜力。通过车辆间的信息交互与协同控制，车辆队列能够实现安全高效跟驰。然而，通信网络的开放性和脆弱性，使得车辆队列容易受到网络攻击的威胁，如拒绝服务 (Denial of Service, DoS) 和虚假数据注入 (False Data Injection, FDI) 等攻击。这些攻击可能导致信息中断或篡改，严重威胁车辆队列系统系统的安全性和稳定性。针对网络攻击下网联车辆队列的弹性控制问题，该文研究由DoS和FDI构成的混合攻击下基于攻击补偿的无模型自适应控制方法。首先，建立异构非线性纵向车辆动力学模型，并构建DoS与FDI共存的混合攻击模型。然后，分析混合攻击对伪梯度参数估计器的影响，设计DoS攻击期间的估计器更新策略，并构建基于历史控制输入信息的攻击补偿机制，以提升DoS攻击下的控制效果。理论分析表明，系统跟踪误差在混合攻击下可实现有界性。仿真实验验证，所提方法有效保障了非线性车辆队列在混合攻击下的跟踪性能。
- 车辆队列 /
- 网络攻击 /
- 弹性控制 /
- 无模型自适应控制
Abstract: Objective Connected and automated vehicle platoons represent a pivotal technology for enhancing traffic efficiency, driving safety, and fuel economy in intelligent transportation systems. Through inter-vehicle information interaction and cooperative control, vehicle platoons can achieve safe and efficient car-following operations. However, their heavy reliance on vehicular communication networks makes them vulnerable to cyberattacks, particularly hybrid threats combining Denial-of-Service (DoS) and False Data Injection (FDI) attacks. Such attacks may lead to the interruption or tampering with information transmission, thus posing a severe threat to the safety and stability of vehicle platoon systems. Simultaneously, vehicle platoon control faces challenges arising from environmental disturbances, parametric uncertainties, and nonlinear dynamic characteristics. Existing model-based control methods often struggle to maintain performance under such complex conditions, necessitating a resilient, data-driven control strategy that does not depend on precise mechanical models. This paper aims to develop a novel attack-compensated Model-Free Adaptive Control (MFAC) framework to ensure the secure and stable operation of heterogeneous nonlinear vehicle platoons under hybrid cyberattacks. Methods Aiming at the resilient control problem of connected vehicle platoons under cyberattacks, this paper proposes an MFAC method based on attack compensation for hybrid attacks involving both DoS and FDI attacks. First, a nonlinear longitudinal vehicle dynamics model for the platoon is established, which is then transformed into an equivalent compact-form dynamic linearized data model via the dynamic linearization technique. This transformation effectively decouples the controller design from the specific mechanical model of the vehicle. In addition, an innovative output tuning factor is introduced to dynamically balance and achieve the simultaneous tracking of both position and velocity states. Second, a hybrid attack model is formulated to capture the characteristics of persistent FDI attacks that inject malicious data and aperiodic DoS attacks that cause communication interruptions. Subsequently, a pseudo-gradient estimator is designed to capture the system dynamics using real-time input-output data; the impact of hybrid attacks on this pseudo-gradient estimator is investigated, and an adaptive update strategy for the estimator is developed during DoS attacks. Most importantly, an intelligent attack compensation mechanism is proposed, which strategically leverages historical control input information during DoS attack periods. This mechanism ensures the continuous and stable operation of the system even when real-time vehicle state information is unavailable, thereby further enhancing the control performance of the connected vehicle platoon system under DoS attacks. Results and Discussions Rigorous theoretical analysis is conducted to prove that the tracking error of the closed-loop system remains bounded under specific conditions regarding the frequency and duration of cyber attacks (Theorem 1). Extensive simulations verify the practical effectiveness of the proposed method. During cyberattacks, the MFAC method with the attack compensation mechanism can adaptively adjust the attenuation rate of its control inputs, thereby effectively guaranteeing the system’s control performance (Fig. 3). Additionally, follower vehicles successfully track the leader’s velocity variations while maintaining the desired inter-vehicle spacing (Fig. 4a, 4b), and the tracking error exhibits satisfactory convergence characteristics (Fig. 4d), thereby verifying the stability of the closed-loop system. Comparative studies demonstrate the critical role of the proposed compensation mechanism: when this mechanism is disabled, the platoon experiences significant performance degradation during cyberattacks (Fig. 5), whereas the proposed method maintains superior tracking accuracy and facilitates faster error recovery. Furthermore, an investigation into the intensity of FDI attacks demonstrates that increasing attack intensity leads to expanded steady-state error bounds (Fig. 6), which not only quantitatively validates the theoretical robustness analysis of the proposed method but also provides important insights for designing security thresholds in practical engineering applications. Conclusions This paper achieves a significant advancement in the secure control of heterogeneous nonlinear connected vehicle platoons by proposing a novel attack-compensated MFAC framework, which effectively addresses the dual challenges of hybrid cyberattacks (i.e., DoS and FDI attacks) and system nonlinearities. Specifically, three key contributions are made to realize this goal: (1) developing a data-driven dynamic linearization framework integrated with an output tuning factor to achieve simultaneous position and velocity tracking, based on the established nonlinear longitudinal vehicle dynamics model and its equivalent data-based linearized model; (2) establishing a hybrid attack model that incorporates aperiodic DoS attacks (causing communication interruptions) and bounded additive FDI attacks (injecting malicious data), capturing their intrinsic characteristics; and (3) designing an intelligent historical input-driven compensation mechanism, coupled with a pseudo-gradient estimator, to optimize control performance during DoS-induced communication outages. Both theoretical analysis and simulation results confirm the effectiveness of the proposed method: the system tracking error can be guaranteed to be bounded when attack parameters satisfy specific conditions, enabling follower vehicles to accurately track the leader’s states while outperforming the compensation-free baseline scheme in velocity tracking accuracy and error convergence speed. Focusing on the hybrid scenario of aperiodic DoS and bounded additive FDI attacks, this work provides a practical model-free solution for enhancing the cybersecurity of connected vehicle platoons. For future research, we will extend the scope to include stealthier hybrid attack modes (non-additive FDI, spoofing and DoS attacks) to explore their coupling mechanisms and design targeted defense strategies. Meanwhile, we will investigate a communication-efficient MFAC strategy that integrates an event-triggered mechanism to reduce network load and improve scalability.
- Vehicle Platoon /
- CyberAttacks /
- Resilient Control /
- Model-Free Adaptive Control

HTML全文

图 1 混合攻击下基于攻击补偿的车辆队列的MFAC框图

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图 2 混合攻击设置

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图 3 混合攻击下跟随车辆的控制输入曲线

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图 4 车辆队列在混合攻击下的表现

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图 5 车辆队列在混合攻击下不应用攻击补偿机制的表现

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图 6 车辆队列的平均跟踪误差在不同程度FDI攻击下的表现

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