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Updated: Mar 23, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Consensus and Stability Analysis of Networked Multiagent Predictive Control Systems.

Guo-Ping Liu

    IEEE Transactions on Cybernetics
    |March 24, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a networked multiagent predictive control scheme to ensure system stability and consensus, even with communication delays and data loss. The findings reveal that stability and consensus are independent of these network imperfections.

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    Last Updated: Mar 23, 2026

    Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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    Published on: February 14, 2025

    1.1K

    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Robotics

    Background:

    • Multiagent control systems face challenges with network-induced issues like communication delays and data loss.
    • Achieving consensus and stability in these systems is crucial for coordinated behavior.
    • Existing methods may struggle to actively compensate for these network imperfections.

    Purpose of the Study:

    • To propose a networked multiagent predictive control scheme.
    • To actively compensate for communication delays and data loss in multiagent systems.
    • To derive conditions for consensus and stability in closed-loop networked systems.

    Main Methods:

    • Development of a predictive control strategy tailored for networked multiagent systems.
    • Mathematical derivation of necessary and sufficient conditions for system consensus and stability.
    • Simulation and analysis using a representative example.

    Main Results:

    • A novel networked multiagent predictive control scheme is presented.
    • Necessary and sufficient conditions for achieving consensus and stability were successfully derived.
    • A key finding is that consensus and stability are independent of communication delays and data loss.

    Conclusions:

    • The proposed networked multiagent predictive control scheme effectively achieves consensus and stability.
    • The control scheme actively compensates for communication delays and data loss.
    • System performance is validated through an illustrative example, demonstrating robustness.