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    Area of Science:

    • Robotics
    • Control Systems
    • Aerospace Engineering

    Background:

    • Flapping-wing vehicles (FWVs) face challenges in formation control due to model uncertainties and measurement inaccuracies.
    • Accurate attitude estimation is crucial for FWV formation but is hindered by accumulating integration errors from angular velocity measurements.

    Purpose of the Study:

    • To develop a robust formation control and state estimation strategy for FWVs.
    • To address model uncertainty, measurement inaccuracy, and attitude estimation divergence in FWV systems.

    Main Methods:

    • A two-layer formation strategy comprising formation control for leaders and containment control for followers.
    • A coupling-based estimation method for attitude angles, leveraging the interplay between translational and rotational motion.
    • Adaptive neural networks integrated with the control and estimation algorithms to compensate for errors.
    • Lyapunov stability theory to guarantee the stability of the control and estimation algorithms.

    Main Results:

    • The proposed formation control strategy effectively manages model uncertainty and measurement inaccuracies.
    • The coupling-based attitude estimation method overcomes the divergence issue caused by integrated angular velocity errors.
    • Adaptive neural networks successfully compensate for model, measurement, and estimation errors, enhancing system robustness.
    • Simulations validate the effectiveness and stability of the developed algorithms.

    Conclusions:

    • The presented approach provides a stable and effective solution for formation control of FWVs.
    • The study demonstrates the feasibility of using coupled motion properties and adaptive neural networks for robust FWV control and estimation.
    • This research contributes to the advancement of autonomous multi-agent systems utilizing FWVs.