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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Event-triggered fuzzy filtering for nonlinear networked systems with dynamic quantization and stochastic cyber

Zhi-Min Li1, Jun Xiong2

  • 1School of Electronic and Control Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China; Hebei Engineering Research Center for Assembly and Inspection Robot, North China Institute of Aerospace Engineering, Langfang 065000, China.

ISA Transactions
|April 16, 2021
PubMed
Summary

This study designs event-triggered H∞ filters for nonlinear networked systems facing cyber attacks and dynamic quantization. The methods ensure system stability and performance using linear matrix inequalities.

Keywords:
Cyber attacksDynamic quantizationEvent-triggered filteringNonlinear networked systemsT–S fuzzy systems

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Area of Science:

  • Control Systems Engineering
  • Networked Systems
  • Fuzzy Logic Systems

Background:

  • Networked systems face challenges from limited bandwidth, dynamic quantization, and stochastic cyber attacks.
  • Takagi-Sugeno (T-S) fuzzy models are used to represent nonlinear networked systems.
  • Event-triggered communication and dynamic quantization are crucial for efficient resource utilization.

Purpose of the Study:

  • To design event-triggered H∞ filters for discrete-time nonlinear networked systems.
  • To address the impact of dynamic quantization and stochastic cyber attacks.
  • To ensure the filtering error system achieves stochastic stability and H∞ performance.

Main Methods:

  • Utilizing Takagi-Sugeno (T-S) fuzzy models for system representation.
  • Implementing an event-triggered communication scheme and a dynamic quantizer.
  • Characterizing cyber attacks using Bernoulli distributed stochastic variables.
  • Employing linear matrix inequalities (LMIs) for filter and quantizer design.

Main Results:

  • Development of full- and reduced-order event-triggered H∞ filters.
  • Design of dynamic quantizer parameters for improved performance.
  • Sufficient conditions for stochastic stability and H∞ performance established via LMIs.
  • Validation of the proposed methods through a practical application example.

Conclusions:

  • The proposed LMI-based approach effectively designs event-triggered H∞ filters and dynamic quantizers for nonlinear networked systems.
  • The methods successfully ensure stochastic stability and H∞ performance under cyber attacks and quantization effects.
  • The study provides a robust framework for robust filtering in resource-constrained networked systems.