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)

Received Date: 2025-10-29
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 key technology for improving traffic efficiency, driving safety, and fuel economy in intelligent transportation systems. Through inter-vehicle information exchange and cooperative control, vehicle platoons achieve safe and efficient car-following operations. However, the strong dependence on vehicular communication networks makes such systems vulnerable to cyberattacks, particularly hybrid threats that combine Denial-of-Service (DoS) and False Data Injection (FDI) attacks. These attacks may interrupt communication or tamper with transmitted information, which threatens the safety and stability of vehicle platoon systems. In addition, vehicle platoon control is affected by environmental disturbances, parametric uncertainties, and nonlinear vehicle dynamics. Existing model-based control methods often experience performance degradation under such complex conditions. Therefore, a resilient data-driven control strategy that does not rely on accurate mechanical models is required. This paper develops an attack-compensated Model-Free Adaptive Control (MFAC) framework to ensure secure and stable operation of heterogeneous nonlinear vehicle platoons under hybrid cyberattacks. Methods To address the resilient control problem of connected vehicle platoons under cyberattacks, an MFAC method with attack compensation is proposed for hybrid attacks that include both DoS and FDI attacks. First, a nonlinear longitudinal vehicle dynamics model of the platoon is established. Using the dynamic linearization technique, the model is converted into an equivalent compact-form dynamic linearized data model. This transformation decouples controller design from the specific mechanical model of the vehicle. An output tuning factor is further introduced to balance the tracking of position and velocity states. Second, a hybrid attack model is constructed to represent persistent FDI attacks that inject malicious data and aperiodic DoS attacks that interrupt communication. A pseudo-gradient estimator is then designed to capture system dynamics from real-time input-output data. The influence of hybrid attacks on this estimator is analyzed, and an adaptive update strategy is proposed for operation during DoS attacks. Finally, an intelligent attack compensation mechanism is designed. During DoS attack periods, the mechanism utilizes historical control input information to maintain controller operation. This design enables the system to operate continuously even when real-time vehicle state information is unavailable and further improves control performance under DoS attacks. Results and Discussions Rigorous theoretical analysis proves that the tracking error of the closed-loop system remains bounded under specific conditions on the frequency and duration of cyberattacks (Theorem 1). Extensive simulations verify the effectiveness of the proposed method. During cyberattacks, the MFAC method with the proposed compensation mechanism adaptively adjusts the attenuation rate of the control input and maintains system control performance (Fig. 3). Follower vehicles successfully track the leader’s velocity variations and maintain the desired inter-vehicle spacing (Fig. 4a, 4b). The tracking error exhibits satisfactory convergence behavior (Fig. 4d), which confirms the stability of the closed-loop system. Comparative studies highlight the role of the compensation mechanism. When the mechanism is disabled, the platoon experiences clear performance degradation during cyberattacks (Fig. 5). In contrast, the proposed method maintains higher tracking accuracy and faster error recovery. Additional simulations analyze the effect of FDI attack intensity. As attack intensity increases, the steady-state error bound expands (Fig. 6). This observation quantitatively supports the theoretical robustness analysis and provides useful guidance for determining security thresholds in applications. Conclusions This paper advances secure control of heterogeneous nonlinear connected vehicle platoons by proposing an attack-compensated MFAC framework. The framework addresses the combined challenges of hybrid cyberattacks (DoS and FDI attacks) and nonlinear system dynamics. Specifically, three key contributions are made: (1) A data-driven dynamic linearization framework is developed, and an output tuning factor is introduced to enable simultaneous position and velocity tracking based on the nonlinear longitudinal vehicle dynamics model and its equivalent data-based linearized model. (2) A hybrid attack model is established that includes aperiodic DoS attacks that interrupt communication and bounded additive FDI attacks that inject malicious data, capturing their essential characteristics. (3) An intelligent historical input-driven compensation mechanism is designed and integrated with a pseudo-gradient estimator to improve control performance during DoS-induced communication interruptions. Theoretical analysis and simulation results confirm the effectiveness of the proposed method. When attack parameters satisfy specific conditions, the system tracking error remains bounded, and follower vehicles accurately track the leader’s states. The proposed method also achieves better velocity tracking accuracy and faster error convergence than the compensation-free baseline scheme. By focusing on hybrid scenarios with aperiodic DoS and bounded additive FDI attacks, this study provides a practical model-free approach to improve cybersecurity in connected vehicle platoons. Future work will examine more stealthy hybrid attack modes, including non-additive FDI, spoofing, and DoS attacks, to analyze their coupling mechanisms and develop targeted defense strategies. In addition, a communication-efficient MFAC strategy that integrates an event-triggered mechanism will be investigated to reduce network load and improve scalability.
- Vehicle Platoon,
- Cyberattacks,
- Resilient Control,
- Model-Free Adaptive Control

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References(37)

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