Aperiodic Sampled-Data-Based Resilient Control for a Class of Switched Nonlinear Systems Against Denial-Of-Service Attacks

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Chunyan Wang, Xinrong Fan, Yifan Fu
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引用次数: 0

Abstract

This paper investigates an aperiodic sampled-data-based resilient control problem for a class of non-strict feedback switched systems under denial-of-service (DoS) attacks. If the output values can be available only at the sampling instants, such incomplete information will make the variable non-differentiable and increase the risk of asynchronous operation for switched systems. Moreover, it will become more serious if DoS attacks destroy both the switching and output signals. To address these challenges, a novel aperiodic sampling rule is designed. Based on the assigned sampling rule, a sampled-data observer and an adaptive resilient controller are constructed, which not only reduce the dual-channel transmission burden, eliminate the trouble of output being not-differentiable, but also overcome the adverse impact of DoS attacks. What's more, the proposed sampling rule and the control strategy, relating to the switching signal, highlight the characteristics of each subsystem. The common Lyapunov stability theory can ensure all the variables of the considered closed-loop switched systems are bounded under arbitrary switching. Finally, the simulation results of two examples are given to verify the effectiveness of the proposed method.

一类切换非线性系统抗拒绝服务攻击的非周期采样数据弹性控制
研究了一类非严格反馈交换系统在拒绝服务攻击下的非周期采样数据弹性控制问题。如果只能在采样时刻获得输出值,则信息不完全会使变量不可微,增加切换系统异步运行的风险。此外,如果DoS攻击同时破坏开关和输出信号,则会变得更加严重。为了解决这些问题,设计了一种新的非周期采样规则。在给定采样规则的基础上,构造了采样数据观测器和自适应弹性控制器,不仅减少了双通道传输负担,消除了输出不可微的困扰,而且克服了DoS攻击的不利影响。此外,针对开关信号,所提出的采样规则和控制策略突出了各子系统的特点。常用的李雅普诺夫稳定性理论可以保证所考虑的闭环切换系统在任意切换下的所有变量都是有界的。最后给出了两个算例的仿真结果,验证了所提方法的有效性。
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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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