Fan Yingjie, Huang Xia, Li Yuxia, Shen Hao
IEEE Trans Neural Netw Learn Syst. 2024 Apr;35(4):5332-5344. doi: 10.1109/TNNLS.2022.3203382. Epub 2024 Apr 4.
This article investigates the sampled-data-based secure synchronization control problem for chaotic Lur'e systems subject to power-constrained denial-of-service (DoS) attacks, which can block data packets' transmission in communication channels. To eliminate the adverse effects, a resilient sampled data control scheme consisting of a secure controller and communication protocol is designed by considering the attack signals and periodic sampling mechanism simultaneously. Then, a novel index, i.e., the maximum anti-attack ratio, is proposed to measure the secure level. On this basis, a multi-interval-dependent functional is established for the resulting closed-loop system model. The main feature of the developed functional lies in that it can fully use the information of resilient sampling intervals and DoS attacks. In combination with the convex combination method, discrete-time Lyapunov theory, and some inequality estimate techniques, two sufficient conditions are, respectively, derived to achieve sampled-data-based secure synchronization of drive-response systems against DoS attacks. Compared with the existing Lyapunov functionals, the advantages of the proposed multi-interval-dependent functional are analyzed in detail. Finally, a synchronization example and an application to secure communication are provided to display the effectiveness and validity of the obtained results.
本文研究了受功率受限拒绝服务(DoS)攻击的混沌Lur'e系统的基于采样数据的安全同步控制问题,该攻击可阻止数据包在通信信道中的传输。为消除不利影响,通过同时考虑攻击信号和周期采样机制,设计了一种由安全控制器和通信协议组成的弹性采样数据控制方案。然后,提出了一种新的指标,即最大抗攻击率,以衡量安全级别。在此基础上,为所得闭环系统模型建立了一个多区间依赖泛函。所开发泛函的主要特点在于它能充分利用弹性采样区间和DoS攻击的信息。结合凸组合方法、离散时间Lyapunov理论和一些不等式估计技术,分别推导了两个充分条件,以实现驱动-响应系统基于采样数据的抗DoS攻击安全同步。与现有Lyapunov泛函相比,详细分析了所提出的多区间依赖泛函的优点。最后,给出了一个同步示例和一个安全通信应用,以展示所得结果的有效性和正确性。