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Nonnegative Consensus Tracking of Networked Systems With Convergence Rate Optimization.

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    Summary
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    This study addresses networked systems consensus tracking using distributed static output-feedback control. New methods optimize convergence rates for multi-input multi-output (MIMO) and single-input multi-output (SIMO) positive dynamic systems.

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems Theory
    • Positive Systems Theory

    Background:

    • Networked systems consensus tracking is crucial for coordinated agent behavior.
    • Designing distributed static output-feedback (SOF) controllers is more complex than state-feedback.
    • Agents are modeled as multi-input multi-output (MIMO) positive dynamic systems, potentially with uncertainties.

    Purpose of the Study:

    • To investigate the nonnegative consensus tracking problem for networked systems using distributed SOF control.
    • To develop conditions for consensus in nominal and uncertain networked positive systems.
    • To propose optimization methods for convergence rates.

    Main Methods:

    • Application of positive systems theory to derive consensus conditions.
    • Development of semidefinite programming (SDP) approaches for MIMO agent convergence rate optimization.
    • Utilization of linear programming (LP) for single-input multi-output (SIMO) agent convergence rate optimization.

    Main Results:

    • Necessary and sufficient conditions for consensus in networked positive systems were established.
    • SDP-based methods were proposed for optimizing MIMO agent consensus convergence.
    • LP-based methods were proposed for optimizing SIMO agent consensus convergence.

    Conclusions:

    • The developed SOF control strategies effectively achieve consensus tracking in networked positive systems.
    • The proposed optimization techniques enhance the convergence speed of agent coordination.
    • Case studies validated the theoretical results and practical applicability of the methods.