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<p>To rationally utilize network resources, three evaluating indicators, two network-load-related threshold indicators and a data transmission-time indicator are designed. Then a novel network-load event-triggered mechanism (NLETM) is proposed based on a switching threshold such that the performance of data transmission can be adjusted dynamically by different network load segments. Two events associated with the network-load-related trigger time and a relaxed threshold are given in the trigger rule to keep away from the Zeno behavior. Based on the NLETM, an adaptive controller with a dynamic compensation mechanism is designed for the cases of (1) uncertain nonlinear systems with unknown constants control coefficients and parameters and (2) unknown time-varying nonlinear systems. The global asymptotic stabilization result of each resulting system is achieved with the performance of data transmission dynamically adjusted with network load. Simulation results underscore the effectiveness of the proposed algorithm.</p>

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Network-load event-trigger design for globally asymptotic stabilization of adaptive nonlinear systems

  • 期刊论文

  • 发表日期：

    2026年5月1日

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  • Guoqing Liu,
  • Yang-Yang Chen,
  • Xiangyu Wang

摘要

To rationally utilize network resources, three evaluating indicators, two network-load-related threshold indicators and a data transmission-time indicator are designed. Then a novel network-load event-triggered mechanism (NLETM) is proposed based on a switching threshold such that the performance of data transmission can be adjusted dynamically by different network load segments. Two events associated with the network-load-related trigger time and a relaxed threshold are given in the trigger rule to keep away from the Zeno behavior. Based on the NLETM, an adaptive controller with a dynamic compensation mechanism is designed for the cases of (1) uncertain nonlinear systems with unknown constants control coefficients and parameters and (2) unknown time-varying nonlinear systems. The global asymptotic stabilization result of each resulting system is achieved with the performance of data transmission dynamically adjusted with network load. Simulation results underscore the effectiveness of the proposed algorithm.