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<p>This paper proposes a novel stability problem for stochastic networked control systems (SNCSs) via truncated predictive control (TPC) in the presence of time-varying delays, sensor distortion, and cyber attacks. First, a dynamic framework for SNCSs is developed that incorporates both uncertainty components and external disturbances. After that, the closed-loop system’s asymptotic stability (AS) with <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(H_{\infty }\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>H</mi> <mi>∞</mi> </msub> </math></EquationSource> </InlineEquation> performance under dual attacks is guaranteed by using the proper Lyapunov–Krasovskii functionals (LKFs) and linear matrix inequalities (LMIs). Notably, this work incorporates a Markov jump process (MJP) to model sensor distortion, providing a more flexible and realistic representation of stochastic switching behavior compared to Bernoulli’s distribution. Dual attacks are modeled via a Bernoulli distribution that governs the switching between deception attack (D-A) and denial-of-service attack (DoS-A) scenarios. To demonstrate the effectiveness of the proposed approach, a simulation study including a mass–spring–damper mechanical system is presented.</p>

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Security-based Truncated Predictive \(H_{\infty }\) Control for Stochastic Networked Control Systems with Sensor Distortion and its Applications

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • T. Narenshakthi,
  • S. Dharani,
  • M. Sivakumar,
  • Yang Cao

摘要

This paper proposes a novel stability problem for stochastic networked control systems (SNCSs) via truncated predictive control (TPC) in the presence of time-varying delays, sensor distortion, and cyber attacks. First, a dynamic framework for SNCSs is developed that incorporates both uncertainty components and external disturbances. After that, the closed-loop system’s asymptotic stability (AS) with \(H_{\infty }\) H ∞ performance under dual attacks is guaranteed by using the proper Lyapunov–Krasovskii functionals (LKFs) and linear matrix inequalities (LMIs). Notably, this work incorporates a Markov jump process (MJP) to model sensor distortion, providing a more flexible and realistic representation of stochastic switching behavior compared to Bernoulli’s distribution. Dual attacks are modeled via a Bernoulli distribution that governs the switching between deception attack (D-A) and denial-of-service attack (DoS-A) scenarios. To demonstrate the effectiveness of the proposed approach, a simulation study including a mass–spring–damper mechanical system is presented.