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<p>This study focuses on investigating the stochastic integral input-to-state stability (SiISS) of stochastic delayed networked control systems with Markovian switching (SDNCSMS). Based on the SiISS-vertex-Lyapunov approach for each node subsystem, we first construct a suitable Lyapunov–Krasovskii functional for a general SDNCSMS. We then combine it with some methods in stochastic analysis to obtain a Lyapunov-type criterion. Following this, we derive another sufficient criterion related to the coefficients in SDNCSMS. Last but not least, the validity and applicability of the proposed approaches are demonstrated through two case studies and their corresponding numerical simulations, including stochastic delayed second-order oscillators with Markovian switching and stochastic delayed Cohen–Grossberg neural networks with Markovian switching.</p>

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Stochastic integral input-to-state stability of stochastic delayed networked control systems with Markovian switching

  • 期刊论文

  • 发表日期：

    2026年5月22日

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  • Yun Zhao,
  • Feifan Huang,
  • Shang Gao

摘要

This study focuses on investigating the stochastic integral input-to-state stability (SiISS) of stochastic delayed networked control systems with Markovian switching (SDNCSMS). Based on the SiISS-vertex-Lyapunov approach for each node subsystem, we first construct a suitable Lyapunov–Krasovskii functional for a general SDNCSMS. We then combine it with some methods in stochastic analysis to obtain a Lyapunov-type criterion. Following this, we derive another sufficient criterion related to the coefficients in SDNCSMS. Last but not least, the validity and applicability of the proposed approaches are demonstrated through two case studies and their corresponding numerical simulations, including stochastic delayed second-order oscillators with Markovian switching and stochastic delayed Cohen–Grossberg neural networks with Markovian switching.