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<p>In networked control systems (NCSs), network congestion and attacks that occur during the data transmission process will lead to a decline in system performance. Thus, ensuring secure and stable operation while simultaneously improving the control performance of systems is crucial. This situation drives us to explore novel control strategies. The event-based control problem for NCSs under deception attacks is investigated on the basis of Takagi–Sugeno fuzzy model in this paper. First, to maximize the utilization efficiency of network resources, a novel adaptive event-triggered (AET) mechanism is proposed. Compared with the existing methods, the proposed AET mechanism determines its threshold through triggering error averaging, which can reduce false triggers while enhancing the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(H_\infty \)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>H</mi> <mi>∞</mi> </msub> </math></EquationSource> </InlineEquation> performance of the fuzzy system. Subsequently, an optimal fuzzy event-triggered-based security controller is designed utilizing the nonparallel distribution compensation principle. Then, sufficient conditions for stochastic stability of the closed-loop system are obtained by the Lyapunov stability theory. Furthermore, to optimize the membership functions (MFs) of the fuzzy controller, an exponential decay learning rate-based MFs iteration strategy is introduced. Compared to fixed learning rate methods, the proposed varying learning rate technique achieves superior <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(H_\infty \)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>H</mi> <mi>∞</mi> </msub> </math></EquationSource> </InlineEquation> performance. Finally, an illustrative simulation example is given to verify the effectiveness of the proposed design approach.</p>

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Event-Triggered Fuzzy Security Control for Networked Control Systems via Improved Membership Functions Iteration Strategy

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Chunyu Hou,
  • Yingnan Pan,
  • Xingjian Sun

摘要

In networked control systems (NCSs), network congestion and attacks that occur during the data transmission process will lead to a decline in system performance. Thus, ensuring secure and stable operation while simultaneously improving the control performance of systems is crucial. This situation drives us to explore novel control strategies. The event-based control problem for NCSs under deception attacks is investigated on the basis of Takagi–Sugeno fuzzy model in this paper. First, to maximize the utilization efficiency of network resources, a novel adaptive event-triggered (AET) mechanism is proposed. Compared with the existing methods, the proposed AET mechanism determines its threshold through triggering error averaging, which can reduce false triggers while enhancing the \(H_\infty \) H ∞ performance of the fuzzy system. Subsequently, an optimal fuzzy event-triggered-based security controller is designed utilizing the nonparallel distribution compensation principle. Then, sufficient conditions for stochastic stability of the closed-loop system are obtained by the Lyapunov stability theory. Furthermore, to optimize the membership functions (MFs) of the fuzzy controller, an exponential decay learning rate-based MFs iteration strategy is introduced. Compared to fixed learning rate methods, the proposed varying learning rate technique achieves superior \(H_\infty \) H ∞ performance. Finally, an illustrative simulation example is given to verify the effectiveness of the proposed design approach.