Event-triggered adaptive secure lateral stabilization for autonomous vehicles under actuator attacks

IF 3.9 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Adaptive Control and Signal Processing Pub Date : 2024-03-24 DOI:10.1002/acs.3797
Hong-Tao Sun, Xinran Chen, Yitao Shen, Chen Peng, Jiwei Zhao
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Abstract

False data injection attacks can disrupt the steering control actions and make a real threat to both the security and safety of autonomous vehicles. In this paper, a secure event-triggered lateral control approach of autonomous vehicles subject to actuator attacks is investigated. Firstly, an arbitrary unknown actuator attacks is considered in the secure lateral steering control of autonomous vehicles. Thus, to save communication resources for the limited bandwidth CAN bus, the periodic event-triggered transmission scheme is utilized, transforming the established lateral steering control into a time-delay system through the consideration of periodic event-triggered sampling. Then, an adaptive control compensation scheme is developed to mitigate the malicious effects caused by actuator attacks. The proposed secure control approach is skilled in compensating the unknown attacked steering control actions in an adaptive way. The stabilization criteria under the adaptive secure control law is well derived by Lyapunov–Krasovskii method and some linear inequality matrices operations. At last, the effectiveness of the proposed secure control scheme is verified by some numerical experiments borrowed from a practical vehicle.

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致动器攻击下自动驾驶车辆的事件触发自适应安全横向稳定功能
虚假数据注入攻击会扰乱转向控制行动,对自动驾驶汽车的安全性构成真正的威胁。本文研究了一种受执行器攻击的自主车辆安全事件触发横向控制方法。首先,在自主车辆的安全横向转向控制中考虑了任意未知执行器攻击。因此,为了节省带宽有限的 CAN 总线的通信资源,采用了周期性事件触发传输方案,通过考虑周期性事件触发采样,将已建立的横向转向控制转化为时延系统。然后,开发了一种自适应控制补偿方案,以减轻执行器攻击造成的恶意影响。所提出的安全控制方法能够以自适应的方式补偿未知的攻击转向控制动作。通过 Lyapunov-Krasovskii 方法和一些线性不等式矩阵运算,很好地推导出了自适应安全控制法则下的稳定准则。最后,借用实际车辆进行了一些数值实验,验证了所提出的安全控制方案的有效性。
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来源期刊
CiteScore
5.30
自引率
16.10%
发文量
163
审稿时长
5 months
期刊介绍: The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.
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