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    This study presents a novel cooperative tracking control for nonlinear multiagent systems with unknown dynamics. A neural network-based dynamic observer ensures follower agents track the leader, provided a connected communication graph.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Artificial Intelligence

    Background:

    • Cooperative tracking is crucial for multiagent systems, enabling coordinated behavior.
    • Existing methods struggle with nonlinear systems, unknown dynamics, and limited communication.
    • Leader-follower architectures are common but require robust control under uncertainty.

    Purpose of the Study:

    • To develop a cooperative dynamic observer for nonlinear multiagent systems with unknown dynamics.
    • To design an observer-based cooperative controller for leader-following tasks under directed graphs.
    • To ensure tracking performance despite local interactions and leader accessibility limitations.

    Main Methods:

    • Utilized a self-structuring neural network (NN) to approximate unknown system dynamics.
    • Introduced a cooperative dynamic observer at each agent node.
    • Employed graph theory, Lyapunov stability analysis, and a separation principle for controller design.

    Main Results:

    • Demonstrated that follower agents can track the leader if the communication graph contains a spanning tree.
    • The proposed framework effectively handles systems with different, unknown dynamics.
    • Simulation results on networked robots validate the control algorithm's effectiveness.

    Conclusions:

    • The observer-based cooperative controller ensures successful tracking in complex multiagent systems.
    • The approach overcomes limitations of traditional tracking control strategies in uncertain environments.
    • Network topology, specifically a spanning tree, is essential for achieving cooperative tracking.