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Distributed dynamic event-triggered algorithm for optimization problem with time delay.

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Summary
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This study introduces a dynamic event-triggered algorithm for multi-agent systems (MAS) to reduce communication. The zero-gradient-sum (ZGS) method enhances efficiency by using minimal state information, preventing Zeno behavior.

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Area of Science:

  • Control Systems Engineering
  • Distributed Systems Optimization
  • Networked Multi-Agent Systems

Background:

  • Optimization problems in multi-agent systems (MAS) often face communication bottlenecks.
  • Reducing communication frequency is crucial for scalability and efficiency in MAS.

Purpose of the Study:

  • To develop an optimized communication strategy for MAS under undirected graphs.
  • To investigate a dynamic event-triggered (DET) mechanism for reducing agent communication frequency.

Main Methods:

  • A zero-gradient-sum (ZGS) algorithm integrated with a dynamic event-triggered (DET) mechanism.
  • Utilizing agent's own state and previous neighbor's state for event-triggering conditions.
  • Employing Lyapunov stability analysis to derive conditions for system stability, incorporating time delays.

Main Results:

  • The proposed DET mechanism significantly reduces communication frequency among agents.
  • The algorithm ensures system stability even with time delays and parameter variations.
  • Zeno behavior is effectively excluded by checking events only at periodic moments.

Conclusions:

  • The developed ZGS algorithm with DET is effective in optimizing MAS communication.
  • The method provides a robust solution for MAS optimization problems with reduced communication overhead.
  • Numerical simulations validate the theoretical findings and demonstrate practical applicability.