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Sampled-data controller scheme for multi-agent systems and its Application to circuit network.

A Stephen1, R Karthikeyan2, C Sowmiya2

  • 1Center for Computational Modeling, Chennai Institute of Technology, Chennai 600 069, India; School of Information and Control Engineering, Kunsan National University, Gunsan-siJeonbuk The Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|December 3, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a new method for synchronizing multi-agent systems (MASs) using sampled-data control, addressing state quantization and time-varying delays. The approach ensures leader-follower synchronization via Linear Matrix Inequalities (LMIs), validated by simulations.

Keywords:
Linear matrix inequalityLooped Lyapunov functionalMulti-agent systemsSampled dataState quantization

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Area of Science:

  • Control Theory
  • Systems Engineering
  • Robotics

Background:

  • Multi-agent systems (MASs) require robust control strategies for coordinated behavior.
  • State quantization and time-varying delays pose significant challenges in achieving synchronization.
  • Existing methods may not adequately address the complexities of sampled-data control in MASs.

Purpose of the Study:

  • To develop synchronization criteria for MASs under sampled-data control.
  • To account for state quantization and time-varying delays in the control design.
  • To ensure reliable synchronization between leader and follower systems.

Main Methods:

  • A novel looped Lyapunov-Krasovskii Functional (LKF) is developed.
  • The LKF integrates sampling interval information for synchronization.
  • Synchronization conditions are formulated as Linear Matrix Inequalities (LMIs).

Main Results:

  • The proposed method establishes clear synchronization criteria.
  • The LMIs provide a computationally tractable condition for controller design.
  • Feasibility and effectiveness are confirmed through numerical simulations and comparative analysis.

Conclusions:

  • The developed sampled-data control method effectively achieves synchronization in MASs.
  • The approach successfully handles state quantization and time-varying delays.
  • The LMI-based solution offers a practical tool for MAS synchronization.