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Parallel event-triggered dynamic output feedback control for nonlinear networked systems with randomly occurring

Jinyuan Zhang1, Yuechao Ma1

  • 1School of Science, Yanshan University, Qinhuangdao, Hebei, 066004, PR China.

ISA Transactions
|May 24, 2024
PubMed
Summary

This study introduces a novel parallel event-triggered (PET) control for networked control systems (NCSs) using T-S fuzzy models. The PET strategy enhances efficiency and stability under communication delays and fading channels.

Keywords:
Discrete-time T–S fuzzy systemMembership function dependenceParallel event-triggered (PET) mechanismRandom multiple communication delays

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Area of Science:

  • Control Systems Engineering
  • Networked Control Systems (NCSs)
  • Fuzzy Logic Systems

Background:

  • Networked Control Systems (NCSs) face challenges with limited bandwidth and unpredictable communication delays.
  • T-S fuzzy models offer a flexible framework for representing complex nonlinear systems.
  • Event-triggered control strategies aim to reduce network load compared to time-triggered approaches.

Purpose of the Study:

  • To design a novel parallel event-triggered (PET) dynamic output feedback controller for discrete-time T-S fuzzy NCSs.
  • To enhance network resource utilization and transmission efficiency through an advanced PET strategy.
  • To ensure system stability and performance under realistic communication impairments.

Main Methods:

  • Design of a novel PET dynamic output feedback controller leveraging relative and absolute triggering conditions.
  • Modeling of random multiple communication delays and the ℓth-order Rice fading channel.
  • Development of sufficient conditions for membership function dependence using staircase function approximation and Lyapunov stability.

Main Results:

  • The proposed PET controller guarantees exponential mean square stability (EMSS) for the NCS.
  • The system achieves guaranteed H∞ performance, indicating robustness against disturbances.
  • The controller effectively handles unmeasurable states and system internal dynamics.

Conclusions:

  • The developed PET dynamic output feedback control strategy is effective for T-S fuzzy NCSs.
  • The approach provides a robust and efficient solution for NCSs operating under communication constraints.
  • Future work will extend this research to T-S fuzzy Markov jump NCSs.