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Distributed time-varying optimization control protocol for multi-agent systems via finite-time consensus approach.

Haojin Li1, Xiaofeng Yue1, Sitian Qin1

  • 1Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 13, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a novel distributed control protocol for multi-agent systems facing time-varying optimization problems with constraints. The method ensures agents reach consensus and track optimal solutions efficiently, even with changing conditions.

Keywords:
Distributed control protocolDistributed time-varying optimizationFinite-time consensusSwitching communication graphs

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Area of Science:

  • Control Systems Engineering
  • Optimization Theory
  • Multi-Agent Systems

Background:

  • Distributed optimization problems in multi-agent systems are challenging due to time-varying constraints and communication graph changes.
  • Existing protocols often struggle with the dynamic nature of these constraints and require global information.
  • Inequality constraints in time-varying optimization pose significant difficulties for achieving consensus and tracking optimal solutions.

Purpose of the Study:

  • To develop a distributed control protocol for solving time-varying optimization problems with inequality constraints in multi-agent systems.
  • To address the challenges posed by switching communication graphs and time-varying inequality constraints.
  • To enable agents to achieve finite-time consensus and track time-varying optimal solutions using only local information.

Main Methods:

  • Employed an exact penalty method and smoothing technique to mitigate the impact of time-varying inequality constraints.
  • Proposed a Hessian-based distributed control protocol utilizing local information and agent interactions.
  • Analyzed the system's convergence properties and consensus behavior under switching communication topologies.

Main Results:

  • Demonstrated that all agents achieve finite-time consensus.
  • Showed that agents successfully track the time-varying global optimal target.
  • Validated the protocol's effectiveness through numerical simulations and a Unmanned Aircraft Vehicle (UAV) moving target tracking experiment.

Conclusions:

  • The proposed Hessian-based distributed control protocol effectively solves distributed time-varying optimization problems with inequality constraints.
  • The protocol is more general than existing methods and exhibits high-efficiency convergence.
  • The approach is validated by practical applications, such as UAV cooperative control for moving target tracking.