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Self-tuning dynamic event-triggered consensus control for multi-agent systems and its implementation via

Zhicheng Hou1, Zhikang Zhou1, Jingcai Zhang1

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Summary
This summary is machine-generated.

This study introduces novel event-triggered control for multi-agent systems, reducing trigger frequency and enhancing convergence. The self-tuning dynamic approach improves system stability and performance.

Keywords:
Dynamic event-triggered controlMulti-agent systemsSelf-triggered control

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Area of Science:

  • Control Theory
  • Systems Engineering
  • Networked Systems

Background:

  • Multi-agent systems require efficient control strategies for coordinated behavior.
  • Event-triggered control reduces communication load compared to time-triggered systems.
  • Existing methods may suffer from high triggering frequencies or complex state monitoring.

Purpose of the Study:

  • To develop a novel self-tuning dynamic event-triggered control for general linear multi-agent systems.
  • To reduce the triggering frequency while improving convergence performance.
  • To eliminate the need for continuous state monitoring and simultaneous neighbor measurements.

Main Methods:

  • A self-tuning dynamic event-triggered control mechanism is proposed.
  • Parameters in Young's inequality are used as design variables for tuning.
  • Lyapunov-based analysis is employed to guarantee system stability and consensus.
  • A self-tuning dynamic self-triggering mechanism is developed to avoid continuous monitoring.

Main Results:

  • Sufficient conditions for asymptotic consensus and Zeno behavior exclusion are derived.
  • The proposed method demonstrates reduced triggering frequency and faster convergence rates.
  • Theoretical and simulation results confirm the stability of the multi-agent system.
  • Comparisons show superior performance over existing state-of-the-art methods.

Conclusions:

  • The novel event-triggered control strategy effectively achieves consensus in multi-agent systems.
  • The self-tuning dynamic approach offers a balance between convergence speed and communication efficiency.
  • The method provides a robust and efficient solution for networked control systems.