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Consensus Tracking of Nonlinear Agents Using Distributed Nonlinear Dynamic Inversion with Switching Leader-Follower

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Summary
This summary is machine-generated.

This study introduces a new consensus tracking protocol using Nonlinear Dynamic Inversion (NDI) for nonlinear agents. The NDI-based controller effectively manages switching topologies and leader-follower connections in multi-agent systems.

Keywords:
consensus trackingdistributed nonlinear dynamic inversionleader–follower consensus

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Distributed Systems

Background:

  • Consensus tracking is crucial for coordinated multi-agent behavior.
  • Existing methods often struggle with dynamic network topologies and leader changes.
  • Nonlinear Dynamic Inversion (NDI) offers advanced control capabilities for complex systems.

Purpose of the Study:

  • To develop and validate a novel consensus tracking protocol for nonlinear agents.
  • To leverage Nonlinear Dynamic Inversion (NDI) for enhanced tracking performance.
  • To address the challenge of dynamic network topologies and switching leader-follower configurations.

Main Methods:

  • Development of a consensus tracking protocol based on Nonlinear Dynamic Inversion (NDI).
  • Mathematical formulation and theoretical analysis of the proposed controller.
  • Simulation studies to evaluate controller performance under various conditions.

Main Results:

  • The proposed NDI-based controller demonstrates effective consensus tracking.
  • The controller successfully handles random switching topologies among agents.
  • Robust performance is achieved despite random switching of leader-follower connections.

Conclusions:

  • The Nonlinear Dynamic Inversion (NDI) technique provides a robust foundation for consensus tracking in nonlinear multi-agent systems.
  • The developed protocol offers a significant advancement for systems with dynamic and unpredictable network configurations.
  • This approach is validated for realistic scenarios involving changing network structures and leadership.