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Consensus of second-order heterogeneous multi-agent systems with and without input saturation.

Mengyao Lu1, Jie Wu1, Xisheng Zhan1

  • 1College of Mechatronics and Control Engineering, Hubei Normal University, Huangshi, 435002, China.

ISA Transactions
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PubMed
Summary

This study addresses consensus in heterogeneous multi-agent systems. Proposed algorithms ensure all agents achieve consensus under specific conditions, verified by simulations.

Keywords:
ConsensusHeterogeneous systemMulti-agent systemsSaturation

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Area of Science:

  • Control Theory
  • Systems Engineering
  • Robotics

Background:

  • Consensus is crucial for coordinated behavior in multi-agent systems.
  • Heterogeneous systems with diverse agent dynamics pose significant challenges.
  • Achieving consensus in systems with input constraints is an open problem.

Purpose of the Study:

  • To investigate the consensus problem in heterogeneous multi-agent systems composed of second-order linear and nonlinear agents.
  • To develop novel algorithms for achieving consensus in such systems, considering input saturation.
  • To provide a theoretical framework and simulation-based validation for the proposed consensus strategies.

Main Methods:

  • Graph theory to model agent interactions and network topology.
  • Lyapunov stability theory to analyze system stability and convergence.
  • Lasalle's invariance principle to establish conditions for consensus achievement.
  • Design of input-saturated and input-unsaturated control algorithms tailored for heterogeneous agents.

Main Results:

  • Guaranteed consensus achievement for all agents in the heterogeneous system.
  • Demonstration of algorithm effectiveness under various system configurations and parameters.
  • Validation of theoretical findings through comprehensive simulation examples.

Conclusions:

  • The proposed algorithms effectively solve the consensus issue for heterogeneous second-order multi-agent systems.
  • The developed methods are robust and applicable to systems with and without input constraints.
  • The study contributes to the advancement of distributed control and coordination strategies for complex multi-agent systems.