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Event-Triggered Sliding Mode Neural Network Controller Design for Heterogeneous Multi-Agent Systems.

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This study enhances multi-agent consensus using event-triggered sliding mode control and neural networks. It reduces controller updates and communication, ensuring robust and efficient system consensus.

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Area of Science:

  • Control Theory
  • Artificial Intelligence
  • Networked Systems

Background:

  • Multi-agent systems face challenges in achieving consensus due to communication and control complexities.
  • Heterogeneous systems with second-order dynamics require robust control strategies.
  • Event-triggered control offers potential for reduced computational and communication loads.

Purpose of the Study:

  • To investigate an event-triggered control strategy for heterogeneous second-order multi-agent consensus problems.
  • To enhance the feasibility and efficiency of consensus protocols.
  • To ensure system robustness against uncertainties.

Main Methods:

  • Sliding mode control (SMC) for inherent robustness.
  • General radial basis function (RBF) neural networks for uncertainty estimation.
  • Event-triggered mechanism to minimize controller and communication updates.
  • Ensuring a lower bound between trigger instants to avoid Zeno behavior.

Main Results:

  • The proposed event-triggered method successfully achieves consensus in heterogeneous second-order multi-agent systems.
  • Controller and communication frequencies are significantly reduced.
  • System robustness is maintained through sliding mode control and neural network-based uncertainty compensation.
  • Simulation results confirm the convergence of consensus errors to zero.

Conclusions:

  • The developed event-triggered sliding mode control approach is effective for heterogeneous second-agent consensus.
  • This method offers a practical solution by reducing communication and computational burdens.
  • The approach demonstrates robustness and efficiency, paving the way for real-world applications.