Constrained model predictive control for networked jump systems under denial-of-service attacks and time delays

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Li Qiu, Runjie Chen, Shaolie Lin, Xueliang Liu, Marzieh Najariyan
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引用次数: 0

Abstract

This article addresses the problem of model predictive control of networked jump systems in the presence of DoS attacks and time delays. In the structural framework of the network predictive control system, we mathematically model the networked jump system by using Markov chains to describe the time delays and a polytope model to describe the jump system phenomenon, considering the properties of DoS attacks and time delays. Based on this, we propose a strategy to lessen the effect of network constraints on the control performance of the system. This strategy involves the corresponding control inputs from the control sequence for real-time active compensation. It includes adjusting the control sequence application length variation based on the duration of the DoS attacks and time delays at each moment. In addition, we demonstrate the recursive feasibility of the control strategy and the global asymptotic stability of the control system from a theoretical perspective through the Lyapunov stability theory. Finally, the effectiveness of the proposed strategy is verified by simulation arithmetic.

拒绝服务攻击和时间延迟下网络跳跃系统的受限模型预测控制
本文探讨了在存在 DoS 攻击和时间延迟的情况下网络跳跃系统的模型预测控制问题。在网络预测控制系统的结构框架中,考虑到 DoS 攻击和时间延迟的特性,我们用马尔可夫链描述时间延迟,用多面体模型描述跳跃系统现象,从而对网络跳跃系统进行数学建模。在此基础上,我们提出了一种减少网络约束对系统控制性能影响的策略。该策略涉及控制序列中的相应控制输入,用于实时主动补偿。它包括根据 DoS 攻击持续时间和每个时刻的时间延迟来调整控制序列应用长度变化。此外,我们还通过 Lyapunov 稳定性理论从理论角度证明了控制策略的递归可行性和控制系统的全局渐近稳定性。最后,通过仿真运算验证了所提策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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