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Modulated sampled-data consensus for networked Euler-Lagrange systems with differentiable pulse function.

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Summary
This summary is machine-generated.

This study introduces differentiable pulse functions to eliminate infinities in sampled-data control for Euler-Lagrange systems. This innovation ensures applicable control inputs and achieves less conservative consensus conditions.

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Area of Science:

  • Control Theory
  • Dynamical Systems
  • Networked Systems

Background:

  • Euler-Lagrange systems are crucial for analyzing dynamical systems.
  • Sampled-data feedback in Euler-Lagrange control laws can generate infinite control inputs.
  • Existing methods face challenges with discontinuous signals in control law differentiation.

Purpose of the Study:

  • To eliminate infinities in control inputs for networked Euler-Lagrange systems.
  • To develop applicable control laws using sampled-data feedback.
  • To establish a new, less conservative consensus condition.

Main Methods:

  • Designing a class of differentiable pulse functions for controllers.
  • Utilizing pulse functions as multipliers to render sampled-data signals differentiable.
  • Conducting rigorous consensus analysis to derive a new consensus condition.

Main Results:

  • Successfully eliminated infinities in control inputs for Euler-Lagrange systems.
  • Developed a novel consensus condition compatible with the proposed pulse functions.
  • The new consensus condition is demonstrated to be less conservative than existing methods.

Conclusions:

  • Differentiable pulse functions effectively resolve infinities in sampled-data Euler-Lagrange control.
  • The developed consensus condition enhances control performance and applicability.
  • Numerical examples validate the theoretical findings and the effectiveness of the proposed approach.