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

HAN Qiaoni; MA Jianguo; LI Peng; ZUO Zhiqiang

doi:10.11999/JEIT251135

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HAN Qiaoni, MA Jianguo, LI Peng, ZUO Zhiqiang. Model-Free Adaptive Resilient Control of Vehicle Platoons Against Hybrid Cyberattacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251135

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HAN Qiaoni, MA Jianguo, LI Peng, ZUO Zhiqiang. Model-Free Adaptive Resilient Control of Vehicle Platoons Against Hybrid Cyberattacks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251135

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

doi: 10.11999/JEIT251135 cstr: 32379.14.JEIT251135

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)

Accepted Date: 2026-02-05
Rev Recd Date: 2026-02-05

Available Online: 2026-02-16

Abstract

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

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References(37)

References

[1]	于树友, 冯阳阳, 曲婷, 等. 车辆队列协同控制综述[J]. 控制与决策, 2024, 39(12): 3889–3909. doi: 10.13195/j.kzyjc.2023.1209. YU Shuyou, FENG Yangyang, QU Ting, et al. A survey of cooperative control of vehicle platoons[J]. Control and Decision, 2024, 39(12): 3889–3909. doi: 10.13195/j.kzyjc.2023.1209.
[2]	吴彦宏, 左志强, 王一晶, 等. 基于数据驱动的智能网联车辆队列控制综述[J]. 控制与决策, 2025, 40(12): 3489–3508. doi: 10.13195/j.kzyjc.2025.0561. WU Yanhong, ZUO Zhiqiang, WANG Yijing, et al. A survey of data-driven control for connected and autonomous vehicles[J]. Control and Decision, 2025, 40(12): 3489–3508. doi: 10.13195/j.kzyjc.2025.0561.
[3]	SUN Xiaoqiang, YU F R, and ZHANG Peng. A survey on cyber-security of connected and autonomous vehicles (CAVs)[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 6240–6259. doi: 10.1109/tits.2021.3085297.
[4]	赵国锋, 吴昊, 王杉杉, 等. 车联网POI查询中的位置隐私和查询隐私联合保护机制[J]. 电子与信息学报, 2024, 46(1): 155–164. doi: 10.11999/JEIT221599. ZHAO Guofeng, WU Hao, WANG Shanshan, et al. A location privacy and query privacy joint protection scheme for POI query in vehicular networks[J]. Journal of Electronics & Information Technology, 2024, 46(1): 155–164. doi: 10.11999/JEIT221599.
[5]	GE Xiaohua, HAN Qinglong, WU Qing, et al. Resilient and safe platooning control of connected automated vehicles against intermittent denial-of-service attacks[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10(5): 1234–1251. doi: 10.1109/JAS.2022.105845.
[6]	JU Zhiyang, ZHANG Hui, LI Xiang, et al. A survey on attack detection and resilience for connected and automated vehicles: From vehicle dynamics and control perspective[J]. IEEE Transactions on Intelligent Vehicles, 2022, 7(4): 815–837. doi: 10.1109/TIV.2022.3186897.
[7]	张晓均, 唐浩宇, 张楠, 等. 分布式智能车载网联系统的匿名认证与密钥协商协议[J]. 电子与信息学报, 2024, 46(4): 1333–1342. doi: 10.11999/JEIT230394. ZHANG Xiaojun, TANG Haoyu, ZHANG Nan, et al. Anonymous authentication and key agreement protocol based on distributed intelligent vehicle networking system[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1333–1342. doi: 10.11999/JEIT230394.
[8]	JU Zhiyang, ZHANG Hui, and TAN Ying. Distributed deception attack detection in platoon-based connected vehicle systems[J]. IEEE Transactions on Vehicular Technology, 2020, 69(5): 4609–4620. doi: 10.1109/TVT.2020.2980137.
[9]	BODDUPALLI S, LIN C W, and RAY S. ReCAP: Protecting cooperative adaptive cruise control against multi-channel perception adversary[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(11): 15702–15717. doi: 10.1109/TITS.2024.3445391.
[10]	ZHAO Yuan, LIU Zhongchang, and WONG W S. Resilient platoon control of vehicular cyber physical systems under DoS attacks and multiple disturbances[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 10945–10956. doi: 10.1109/TITS.2021.3097356.
[11]	ZHAO Ning, ZHAO Xudong, CHEN Meng, et al. Resilient distributed event-triggered platooning control of connected vehicles under denial-of-service attacks[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 6191–6202. doi: 10.1109/TITS.2023.3250402.
[12]	宋秀兰, 李洋阳, 何德峰. 外部干扰和随机DoS攻击下的网联车安全H_∞队列控制[J]. 自动化学报, 2024, 50(2): 348–355. doi: 10.16383/j.aas.c230327. SONG Xiulan, LI Yangyang, and HE Defeng. Secure H_∞ platooning control for connected vehicles subject to external disturbance and random DoS attacks[J]. Acta Automatica Sinica, 2024, 50(2): 348–355. doi: 10.16383/j.aas.c230327.
[13]	苗金钊, 刘金良, 孙乐, 等. 基于虚假数据检测的信息物理系统安全学习控制方法[J]. 电子与信息学报. doi: 10.11999/JEIT250537. (查阅网上资料,未找到年份卷期页码信息,请补充). MIAO Jinzhao, LIU Jinliang, SUN Le, et al. A learning-based security control method for cyber-physical systems based on false data detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250537.
[14]	WU Yanhong, ZUO Zhiqiang, WANG Yijing, et al. Distributed data-driven model predictive control for heterogeneous vehicular platoon with uncertain dynamics[J]. IEEE Transactions on Vehicular Technology, 2023, 72(8): 9969–9983. doi: 10.1109/TVT.2023.3262705.
[15]	MA Yongsheng, CHE Weiwei, DENG Chao, et al. Data-driven distributed vehicle platoon control for heterogeneous nonlinear vehicle systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(3): 2373–2381. doi: 10.1109/TITS.2023.3321750.
[16]	HAN Qiaoni, MA Jianguo, ZUO Zhiqiang, et al. Data-driven finite-time platooning control for heterogeneous nonlinear vehicle systems[J]. IEEE Control Systems Letters, 2025, 9: 33–37. doi: 10.1109/LCSYS.2025.3552676.
[17]	侯忠生, 金尚泰. 无模型自适应控制——理论与应用[M]. 北京: 科学出版社, 2013. HOU Zhongsheng and JIN Shangtai. Model-Free Adaptive Control: Theory and Applications[M]. Beijing: Science Press, 2013. (查阅网上资料, 未找到对应的英文翻译, 请确认).
[18]	HOU Zhongsheng and JIN Shangtai. A novel data-driven control approach for a class of discrete-time nonlinear systems[J]. IEEE Transactions on Control Systems Technology, 2011, 19(6): 1549–1558. doi: 10.1109/TCST.2010.2093136.
[19]	HOU Zhongsheng and JIN Shangtai. Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems[J]. IEEE Transactions on Neural Networks, 2011, 22(12): 2173–2188. doi: 10.1109/TNN.2011.2176141.
[20]	YU Wei, WANG Rui, BU Xuhui, et al. Resilient model-free adaptive iterative learning control for nonlinear systems under periodic DoS attacks via a fading channel[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(7): 4117–4128. doi: 10.1109/TSMC.2021.3091422.
[21]	CHEN Runze, LI Luanxin, and HOU Zhongsheng. Distributed model-free adaptive control for multi-agent systems with external disturbances and DoS attacks[J]. Information Sciences, 2022, 613: 309–323. doi: 10.1016/j.ins.2022.09.035.
[22]	MA Yongsheng, CHE Weiwei, DENG Chao, et al. Distributed model-free adaptive control for learning nonlinear MASs under DoS attacks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(3): 1146–1155. doi: 10.1109/TNNLS.2021.3104978.
[23]	DENG Chao, JIN Xiaozheng, WU Zhengguang, et al. Data-driven-based cooperative resilient learning method for nonlinear MASs under DoS attacks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(9): 12107–12116. doi: 10.1109/TNNLS.2023.3252080.
[24]	BU Xuhui, GUO Jinli, CUI Lizhi, et al. Event-triggered model-free adaptive containment control for nonlinear multiagent systems under DoS attacks[J]. IEEE Transactions on Control of Network Systems, 2024, 11(4): 1845–1857. doi: 10.1109/TCNS.2023.3290071.
[25]	ZHU Panpan, JIN Shangtai, BU Xuhui, et al. Model-free adaptive control for a class of MIMO nonlinear cyberphysical systems under false data injection attacks[J]. IEEE Transactions on Control of Network Systems, 2023, 10(1): 467–478. doi: 10.1109/TCNS.2022.3203354.
[26]	LI Fanghui and HOU Zhongsheng. Distributed model-free adaptive control for MIMO nonlinear multiagent systems under deception attacks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(4): 2281–2291. doi: 10.1109/TSMC.2022.3211871.
[27]	WANG Yueming, LI Yuanxin, TONG Shaocheng, et al. Data-driven-based event-triggered prescribed performance tracking of nonlinear system with FDI attacks[J]. IEEE Transactions on Automation Science and Engineering, 2024, 21(4): 5357–5366. doi: 10.1109/TASE.2023.3310621.
[28]	SUN Shanshan, LI Yuanxin, and HOU Zhongsheng. Data-driven reinforcement learning tracking of MASs under injection attack: A controller-dynamic-linearization approach[J]. IEEE Transactions on Fuzzy Systems, 2024, 32(11): 6069–6078. doi: 10.1109/TFUZZ.2024.3439351.
[29]	ZHU Panpan, JIN Shangtai, BU Xuhui, et al. Distributed data-driven control for a connected autonomous vehicle platoon subjected to false data injection attacks[J]. IEEE Transactions on Automation Science and Engineering, 2024, 21(4): 7527–7538. doi: 10.1109/TASE.2023.3345369.
[30]	YUE Baifan and CHE Weiwei. Data-driven fault-tolerant platooning control under aperiodic DoS attacks[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(11): 12166–12179. doi: 10.1109/TITS.2023.3286403.
[31]	FENG Shuai and TESI P. Resilient control under denial-of-service: Robust design[J]. Automatica, 2017, 79: 42–51. doi: 10.1016/j.automatica.2017.01.031.
[32]	LIU Jinliang, GU Yuanyuan, ZHA Lijuan, et al. Event-triggered H_∞ load frequency control for multiarea power systems under hybrid cyber attacks[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(8): 1665–1678. doi: 10.1109/TSMC.2019.2895060.
[33]	LI Xin, WEI Guoliang, DING Derui, et al. Recursive filtering for time-varying discrete sequential systems subject to deception attacks: Weighted try-once-discard protocol[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(6): 3704–3713. doi: 10.1109/TSMC.2021.3064653.
[34]	LI Zhanjie and ZHAO Jun. Resilient adaptive control of switched nonlinear cyber-physical systems under uncertain deception attacks[J]. Information Sciences, 2021, 543: 398–409. doi: 10.1016/j.ins.2020.07.022.
[35]	LI Fanghui and HOU Zhongsheng. Learning-based model-free adaptive control for nonlinear discrete-time networked control systems under hybrid cyber attacks[J]. IEEE Transactions on Cybernetics, 2024, 54(3): 1560–1570. doi: 10.1109/TCYB.2022.3225203.
[36]	LIU Haotian, WU Wenchuan, and BOSE A. Model-free voltage control for inverter-based energy resources: Algorithm, simulation and field test verification[J]. IEEE Transactions on Energy Conversion, 2021, 36(2): 1207–1215. doi: 10.1109/TEC.2020.3025758.
[37]	LUO Qianyue, NGUYEN A T, FLEMING J, et al. Unknown input observer based approach for distributed tube-based model predictive control of heterogeneous vehicle platoons[J]. IEEE Transactions on Vehicular Technology, 2021, 70(4): 2930–2944. doi: 10.1109/TVT.2021.3064680.