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    This study achieves scaled position consensus in complex multiagent systems. A novel distributed adaptive algorithm ensures agents reach scaled consensus despite uncertainties and switching network topologies.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Robotics

    Background:

    • Achieving consensus in multiagent systems is crucial for coordinated behavior.
    • High-order systems and directed graphs present significant control challenges.
    • Scaled position consensus, where agents agree on scaled states, is less explored.

    Purpose of the Study:

    • To develop a distributed adaptive algorithm for scaled position consensus in high-order multiagent systems.
    • To address challenges including parametric uncertainties, switching directed graphs, and limited information exchange.
    • To enable fully distributed control without shared or global gains.

    Main Methods:

    • Design of a high-order reference model for each agent using relative scaled position information.
    • Proposal of a transformation to simplify scaled position consensus from high-order to first-order systems.
    • Development of a distributed adaptive algorithm based on an MRACon scheme.

    Main Results:

    • Theoretical analysis confirms the achievement of scaled consensus over switching directed graphs.
    • Numerical simulations validate the proposed algorithm's effectiveness.
    • Demonstration of collective behaviors like traditional, bipartite, and cluster consensus by adjusting agent scales.

    Conclusions:

    • The proposed distributed adaptive algorithm effectively achieves scaled position consensus in complex multiagent systems.
    • The method is robust to parametric uncertainties and operates over uniformly jointly connected switching directed graphs.
    • The approach facilitates diverse collective behaviors through precise scale selection.