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Two-Layer Distributed Formation-Containment Control of Multiple Euler-Lagrange Systems by Output Feedback.

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    This study tackles the distributed formation-containment problem for Euler-Lagrange systems. It introduces a novel framework using estimators and adaptive neural networks to achieve robust control with model uncertainties.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Applied Mathematics

    Background:

    • Distributed formation-containment (DFC) is crucial for multi-agent systems.
    • Euler-Lagrange systems are common in robotics and mechanical systems.
    • Model uncertainties and lack of velocity sensors pose significant control challenges.

    Purpose of the Study:

    • To address the distributed formation-containment (DFC) problem for multiple Euler-Lagrange systems with model uncertainties.
    • To propose a novel two-layer framework for DFC.
    • To handle scenarios with partial state information from a dynamic leader.

    Main Methods:

    • A distributed finite-time sliding-mode estimator is designed for state estimation.
    • High-gain observers are integrated with DFC control laws for leaders and followers.
    • Adaptive neural networks are employed to manage model uncertainties.
    • Lyapunov stability theory is used to guarantee boundedness of state errors.

    Main Results:

    • Accurate estimations of desired position and velocity are achieved for each agent.
    • Constant and time-varying formations are successfully realized.
    • Leader-based containment in the second layer and time-varying formation in the first layer are achieved.
    • Uniform ultimate boundedness of all state errors is guaranteed.

    Conclusions:

    • The proposed DFC approach effectively handles model uncertainties in Euler-Lagrange systems.
    • The novel two-layer framework provides a unified approach for various distributed control problems.
    • Simulation results validate the feasibility and effectiveness of the developed control strategies.