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<p>This paper addresses the problem of dynamic quantized output feedback <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(H_\infty \)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>H</mi> <mi>∞</mi> </msub> </math></EquationSource> </InlineEquation> control for nonlinear networked control systems (NCSs) under denial-of-service (DoS) attacks. A T-S fuzzy model is employed to approximate nonlinear dynamics subject to system uncertainties and external disturbances. To alleviate communication constraints, a dynamic quantizer is designed at the measurement output, while the random occurrence of DoS attacks on the transmission channel is modeled by a binary Markov chain. By applying the Lyapunov stability theory and linear matrix inequality (LMI) techniques, sufficient conditions ensuring the stochastic stability of the closed-loop systems are derived. The corresponding controller gains and quantizer parameters are then obtained through convex optimization. Finally, simulation results verify the effectiveness and robustness of the proposed control strategy.</p>

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Dynamic quantized output feedback \(H_\infty \) control of T-S fuzzy networked control systems under DoS attacks

  • 期刊论文

  • 发表日期：

    2026年6月14日

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  • Cheng Tan,
  • Yingchun Zhu,
  • Ziran Chen,
  • Xiangyong Chen

摘要

This paper addresses the problem of dynamic quantized output feedback \(H_\infty \) H ∞ control for nonlinear networked control systems (NCSs) under denial-of-service (DoS) attacks. A T-S fuzzy model is employed to approximate nonlinear dynamics subject to system uncertainties and external disturbances. To alleviate communication constraints, a dynamic quantizer is designed at the measurement output, while the random occurrence of DoS attacks on the transmission channel is modeled by a binary Markov chain. By applying the Lyapunov stability theory and linear matrix inequality (LMI) techniques, sufficient conditions ensuring the stochastic stability of the closed-loop systems are derived. The corresponding controller gains and quantizer parameters are then obtained through convex optimization. Finally, simulation results verify the effectiveness and robustness of the proposed control strategy.