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Resource allocation over nonlinear uncertain multi-agent systems: An efficient communication perspective.

Haoze Li1, Chenjie Yan1, Sitian Qin1

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

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|February 6, 2026
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Summary
This summary is machine-generated.

This study introduces a new distributed algorithm for multi-agent systems (MAS) to overcome limitations like resource constraints and communication issues. The algorithm enhances system robustness and communication efficiency in uncertain environments.

Keywords:
Dynamic event-triggered mechanismNonlinear uncertain multi-agent systemResource allocationWeight-unbalanced directed graph

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Area of Science:

  • Control Engineering
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Multi-agent systems (MAS) face operational challenges including resource limitations, communication bandwidth constraints, and environmental changes.
  • These challenges can lead to nonlinear behavior, uncertain parameters, and unbalanced communication within MAS.
  • Existing frameworks for distributed resource allocation in MAS require robust solutions to these inherent operational issues.

Purpose of the Study:

  • To propose a novel distributed algorithm for multi-agent systems (MAS) that addresses nonlinear behavior, uncertain parameters, and asymmetric communication.
  • To enhance the robustness and application scope of MAS in dynamic and uncertain operational environments.
  • To improve communication efficiency in MAS by integrating a dynamic event-triggered mechanism.

Main Methods:

  • Development of a distributed algorithm comprising a controller and an optimal estimator.
  • Incorporation of a dynamic compensation mechanism and a tracking technique to handle system nonlinearities and parameter uncertainties.
  • Integration of a dynamic event-triggered mechanism with the dynamic compensation mechanism to optimize communication resource usage.

Main Results:

  • The proposed algorithm effectively handles system nonlinearities and tracks uncertain parameters.
  • Asymmetric communication is compensated for, enhancing overall system robustness.
  • The dynamic event-triggered mechanism significantly improves communication efficiency, especially under unbalanced communication topologies.

Conclusions:

  • The novel distributed algorithm provides a robust solution for MAS operating under challenging conditions.
  • The integration of dynamic compensation and event-triggered mechanisms expands the applicability of MAS.
  • Validation through a five-drone cooperative formation case study confirms the algorithm's effectiveness in nonlinear, uncertain MAS.