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    This study presents iterative learning control (ILC) for flexible micro aerial vehicles (MAVs) facing disturbances. The proposed control effectively suppresses wing vibrations and ensures stable flight trajectories.

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    Area of Science:

    • Robotics and Control Systems
    • Aerospace Engineering
    • Applied Mathematics

    Background:

    • Flexible micro aerial vehicles (MAVs) are susceptible to spatiotemporally varying disturbances.
    • Controlling MAVs with coupled bending and twisting wing dynamics presents significant challenges.

    Purpose of the Study:

    • To develop and validate iterative learning control (ILC) schemes for a flexible MAV.
    • To suppress wing vibrations (bending and twisting) and reject external disturbances.
    • To regulate the MAV's rigid body displacement to follow a constant trajectory.

    Main Methods:

    • Modeling the flexible MAV using Hamilton's principle, capturing coupled bending-twisting dynamics.
    • Designing two iterative learning control (ILC) algorithms.
    • Utilizing a composite energy function to prove system boundedness and convergence.

    Main Results:

    • The proposed ILC schemes effectively suppress vibrations in both bending and twisting modes.
    • Distributed disturbances are successfully rejected, enhancing flight stability.
    • The rigid body displacement accurately tracks the desired constant trajectory.

    Conclusions:

    • The developed ILC laws are effective for controlling flexible MAVs under challenging conditions.
    • The theoretical analysis confirms the boundedness and learning convergence of the closed-loop system.
    • Simulation results validate the practical applicability of the proposed control strategies.