DoS攻击下马尔可夫跳跃系统的动态事件触发故障检测:一种基于模拟退火算法的优化方法

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yi Wang;Peng Cheng;Di Wu;Weidong Zhang;Edmond Q. Wu;Feng Shu
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引用次数: 0

摘要

研究了基于动态事件触发机制的马尔可夫跳变系统在拒绝服务攻击下的故障检测观测器设计问题。利用攻击者能量有限的概念来描述非周期性DoS攻击的特性。为了节省系统的通信资源,引入了动态事件触发机制。引入$H_{\infty }/H_{-}$指标以确保设计的FDO具有对干扰的鲁棒性和对故障的敏感性。在利用Lyapunov泛函技术得到一组非线性不等式后,采用模拟退火算法辅助求解,既保证了全局最优解的发现,又保证了动态事件触发机制得到满意的参数。最后,以垂直起降飞行器动力系统为例,验证了所设计FDO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。
Dynamic Event-Triggered Fault Detection for Markov Jump Systems Under DoS Attacks: A Simulated Annealing Algorithm-Based Optimization Approach
This work addresses the design problem of the fault detection observer (FDO) based on dynamic event-triggered mechanism for Markov jump systems under denial-of-service (DoS) attacks. The concept of limited energy for attackers is employed to characterize the property of nonperiodic DoS attacks. A dynamic event-triggered mechanism is introduced to save the system’s communication resources. The $H_{\infty }/H_{-}$ index is incorporated to ensure that the designed FDO possesses both robustness against disturbances and sensitivity to faults. After obtaining a set of nonlinear inequalities using Lyapunov functional techniques, a simulated annealing algorithm is employed to assist in solving, ensuring not only the discovery of global optimization solutions but also obtaining satisfactory parameters for the dynamic event-triggered mechanism. Finally, the effectiveness of the designed FDO is illustrated by an example of a vertical take-off and landing vehicle dynamical system.
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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