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    This study presents a data-driven method for optimal consensus tracking in heterogeneous linear multiagent systems. The approach uses Q-learning to achieve cooperative control without needing system models, ensuring effective tracking performance.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Game Theory

    Background:

    • Heterogeneous linear multiagent systems require advanced control strategies for coordinated tasks.
    • Optimal consensus tracking is crucial for applications like distributed robotics and sensor networks.

    Purpose of the Study:

    • To develop a model-free optimal consensus tracking control for heterogeneous linear multiagent systems.
    • To reformulate the tracking problem as a Nash equilibrium solution in multiplayer games.

    Main Methods:

    • Introduction of tracking error dynamics and coupled Hamilton-Jacobi equations.
    • Design of a data-based error estimator for control.
    • Application of input-output (I/O) Q-learning with value iteration for optimal cooperative control.

    Main Results:

    • A novel data-based control law is derived for optimal consensus tracking.
    • The control law effectively utilizes measured input-output information.
    • The method does not require prior knowledge of the multiagent system models.

    Conclusions:

    • The proposed Q-learning based approach provides an effective solution for optimal consensus tracking in complex multiagent systems.
    • The model-free nature of the controller enhances its applicability in real-world scenarios.
    • Numerical simulations validate the algorithm's performance and effectiveness.