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Privacy-Preserving Average-Tracking Control for Multi-Agent Systems with Constant Reference Signals.

Wei Jiang1, Cheng-Lin Liu2

  • 1School of Mechanical, Electrical and Information Engineering, Wuxi Vocational Institute of Arts & Technology, Wuxi 214206, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel privacy-preserving algorithm for multi-agent systems to achieve average tracking, even with delayed information. The research establishes conditions for reliable convergence in tracking control systems.

Keywords:
constant reference signalsintegral-type average-tracking algorithmmismatched reference signalsprivacy-preserving

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Area of Science:

  • Control Theory
  • Distributed Systems
  • Networked Systems

Background:

  • Multi-agent systems require robust control strategies for coordinated behavior.
  • Average-tracking is crucial for tasks like distributed estimation and resource allocation.
  • Existing methods may lack privacy preservation or struggle with system delays.

Purpose of the Study:

  • To develop a privacy-preserving average-tracking control algorithm for multi-agent systems.
  • To analyze the convergence properties of the proposed algorithm under constant reference signals.
  • To extend the algorithm for handling mismatched reference signals while maintaining privacy.

Main Methods:

  • Introduction of auxiliary signals incorporating agent states and delayed states.
  • Development of a novel integral-type average-tracking algorithm.
  • Application of frequency-domain analysis to derive delay-dependent conditions.
  • Extension of the algorithm to address mismatched reference signals.

Main Results:

  • A privacy-preserving integral-type average-tracking algorithm is proposed.
  • Delay-dependent sufficient and necessary conditions for asymptotic average-tracking convergence are derived.
  • An extension guarantees privacy-preserving average-tracking convergence with mismatched references.
  • Numerical simulations validate the algorithm's effectiveness.

Conclusions:

  • The proposed algorithm effectively achieves privacy-preserving average tracking in multi-agent systems.
  • The derived conditions ensure robust convergence despite system delays.
  • The approach is adaptable to more complex scenarios with mismatched references.