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Model-Free Adaptive Iterative Learning Bipartite Containment Control for Multi-Agent Systems.

Shangyu Sang1, Ruikun Zhang1, Xue Lin1

  • 1School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China.

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Summary
This summary is machine-generated.

This study introduces a model-free adaptive iterative learning control for nonlinear multi-agent systems to achieve bipartite containment tracking. The method ensures follower states converge to a convex hull of leader states without needing system models.

Keywords:
model-free adaptive iterative learning controlmulti-agent systemssigned networks

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Area of Science:

  • Control Theory
  • Systems Engineering
  • Robotics

Background:

  • Multi-agent systems (MASs) present complex interaction dynamics.
  • Bipartite containment tracking is crucial for coordinated behaviors in MASs.
  • Existing methods often require detailed system models.

Purpose of the Study:

  • To develop a novel control strategy for bipartite containment tracking in nonlinear MASs.
  • To propose a model-free approach, reducing reliance on system identification.
  • To ensure asymptotic convergence of follower states to a defined target set.

Main Methods:

  • Dynamic linearization method applied to nonlinear MASs.
  • Novel model-free adaptive iterative learning control (MFAILC) design.
  • Analysis of convergence conditions for bipartite containment error.

Main Results:

  • The MFAILC controller effectively solves the bipartite containment tracking problem.
  • Controller performance is independent of the MASs' model information.
  • Containment error asymptotically converges to zero, validating the control strategy.

Conclusions:

  • The proposed MFAILC is effective for bipartite containment tracking in nonlinear MASs.
  • The model-free nature simplifies practical implementation.
  • Simulation results confirm the controller's efficacy and robustness.