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Event-triggered H∞ filter design for sampled-data systems with quantization.

Gang Chen1, Yun Chen1, Hong-Bing Zeng1

  • 1School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China.

ISA Transactions
|February 19, 2020
PubMed
Summary
This summary is machine-generated.

This study presents an event-triggered H∞ filter for sampled-data systems, reducing network load. The method ensures stability and performance using Lyapunov-Krasovskii functionals and linear matrix inequalities.

Keywords:
Event-triggered controlH filterQuantizationSampled-data systems

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Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Networked Systems

Background:

  • Event-triggered mechanisms offer resource savings over time-triggered systems.
  • Quantization in sampled-data systems impacts filter performance.
  • H∞ filter design is crucial for robust control.

Purpose of the Study:

  • To design an H∞ filter for sampled-data systems incorporating quantization and event-triggered control.
  • To develop a novel filtering error model considering quantization effects.
  • To reduce network resource utilization through an event-triggered approach.

Main Methods:

  • An event-triggered mechanism for data packet release.
  • A time interval analysis approach for the sampled-data filtering error model.
  • Lyapunov-Krasovskii functional (LKF) and linear matrix inequality (LMI) techniques.

Main Results:

  • A new sampled-data filtering error model accounting for quantization.
  • Conditions derived using LKF and LMIs for asymptotical stability and H∞ performance.
  • Co-design of event-triggered and H∞ filter parameters.

Conclusions:

  • The proposed event-triggered H∞ filter design is efficient for sampled-data systems with quantization.
  • The approach conserves network resources compared to traditional methods.
  • Validated through a mass-spring system example.