基于事件触发机制的混合攻击T-S模糊网络系统H_∞控制方法

H_∞ control method for T-S fuzzy networked systems under hybrid attacks based on event-triggered communication mechanism

摘要

为解决T-S模糊网络系统在开放环境中易受混合网络攻击、存在安全威胁和通信资源受限问题,提出一种基于事件触发机制的T-S模糊网络系统H_∞控制方法。采用双伯努利随机变量刻画网络攻击的随机特性,构建耦合欺骗攻击与重放攻击的随机混合攻击模型,引入离散事件触发机制,在保障系统动态性能的前提下缓解通信带宽压力;构建低保守性Lyapunov-Krasovskii泛函,给出保证闭环系统均方渐近稳定且满足给定H_∞扰动衰减性能指标的充分条件,实现抗攻击H_∞控制器与事件触发机制优化设计。仿真结果表明:基于事件触发机制的混合攻击T-S模糊网络系统H_∞控制方法能够在抵御混合攻击的同时,有效降低数据传输频率和通信资源占用。该结果为通信资源受限条件下网络化控制系统的安全控制设计提供参考。

Abstract

To solve the problems that T-S fuzzy networked systems in open environments are vulnerable to mixed cyberattacks, face security threats, and have limited communication resources, an event-triggered mechanism-based H_∞ control method for T-S fuzzy networked systems is proposed. Double Bernoulli random variables are employed to characterize the stochastic nature of cyberattacks, constructing a stochastic mixed attack model that couples deception attacks and replay attacks. Meanwhile, a discrete event-triggered mechanism is introduced to alleviate communication bandwidth pressure while guaranteeing the dynamic performance of the system. Based on a low-conservatism Lyapunov-Krasovskii functional and linear matrix inequalities (LMIs), sufficient conditions are derived to guarantee that the closed-loop system is mean-square asymptotically stable and strictly satisfies a given H_∞ disturbance attenuation performance index, thereby achieving the co-design of the resilient H_∞ controller and the event-triggered mechanism. Simulation results demonstrate that the proposed H_∞ control method for T-S fuzzy networked systems can effectively reduce data transmission frequency and communication resource occupation while resisting mixed attacks. This result provides a reference for the secure control design of networked control systems under limited communication resources.

关键词

T-S模糊网络系统 / 事件触发机制 / 重放攻击 / 欺骗攻击 / H_∞控制

Key words

T-S fuzzy networked systems / ETCM / replay attacks / deceptive attacks / H_∞ control

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基金资助

海南省重点研发项目(ZDYF2025GXJS002)

河北省自然科学基金面上项目(F2023107002)