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Distributed adaptive barrier function-based integral sliding mode consensus control of multi-agent systems.

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  • 1National Institute of Technology, Silchar, 788010, Assam, India.

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Summary
This summary is machine-generated.

This study introduces novel distributed adaptive control protocols for multi-agent systems, enhancing leader-follower consensus and time-varying formations. The barrier function-based approach minimizes control gains and reduces chattering for improved stability.

Keywords:
Barrier function-based controlDirected graphsDistributed adaptive consensus controlIntegral sliding mode controlLeader-follower consensusTime-varying formation

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Distributed Systems

Background:

  • Multi-agent systems require robust control for consensus and formation tasks.
  • Existing methods often struggle with unknown disturbances and controller gain tuning.

Purpose of the Study:

  • To design distributed adaptive control protocols for leader-follower consensus and time-varying formation problems.
  • To address uncertain linear dynamics and unknown bounded disturbances in multi-agent systems.
  • To reduce control chattering and avoid gain overestimation.

Main Methods:

  • Development of projection operator-based adaptive control protocols.
  • Implementation of an integral sliding mode-based robust control strategy.
  • Design of a barrier function-based adaptive integral sliding-mode controller to adjust discontinuous control gains.

Main Results:

  • The proposed control technique ensures convergence of disagreement variables to a predefined neighborhood of zero.
  • Lyapunov-based stability proofs confirm convergence for consensus and formation control.
  • Numerical examples validate the effectiveness of the developed protocols.

Conclusions:

  • The novel adaptive control protocols effectively achieve leader-follower consensus and time-varying formations in multi-agent systems.
  • The barrier function approach successfully mitigates chattering and improves control performance.
  • The findings offer a robust solution for complex multi-agent coordination problems.