虚假数据注入攻击下网络化人工胰腺系统的记忆容错T-S模糊控制

IF 2.7 1区 数学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Shen Yan , Liming Ding , Yue Cai
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引用次数: 0

摘要

研究了基于内存的网络化人工胰腺系统在虚假数据注入攻击下的容错控制问题。首先,利用T-S模糊建模方法和基于采样输出的T-S模糊观测器分别处理系统非线性和估计系统全状态。其次,为了在通信网络存在恶意攻击的情况下保持系统的安全性,引入了基于径向基函数神经网络的估计器来逼近真实攻击信号。然后,利用估计状态、与采样周期相关的记忆状态和近似攻击,构造了基于记忆的容错T-S模糊控制器,使血糖恢复到安全范围内。第三,导出了在一组线性矩阵不等式中形成的求解控制器和观测器增益的新充分条件。最后,通过说明性仿真结果验证了所提出的血糖调节方案的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Memory-based attack-tolerant T-S fuzzy control of networked artificial pancreas system subject to false data injection attacks
This paper studies the memory-based attack-tolerant control issue of networked artificial pancreas system subject to false data injection attacks. First, a T-S fuzzy modeling approach and a T-S fuzzy observer based on sampled outputs are utilized to deal with the system nonlinearity and estimate full system state, respectively. Second, to maintain the system security in the presence of malicious attacks in the communication network, a radial basis function neural network-based estimator is introduced to approximate the real attack signal. Then, with the utilization of estimated state, memory state related to sampled period and approximated attack, a memory-based attack-tolerant T-S fuzzy controller is constructed to restore the blood glucose levels within a safe range. Third, some novel sufficient conditions formed in a set of linear matrix inequalities are derived to solve the controller and observer gains. Finally, the advantages of the proposed blood glucose regulation scheme are confirmed through illustrative simulation outcomes.
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来源期刊
Fuzzy Sets and Systems
Fuzzy Sets and Systems 数学-计算机:理论方法
CiteScore
6.50
自引率
17.90%
发文量
321
审稿时长
6.1 months
期刊介绍: Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.
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