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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Aerospace Engineering

    Background:

    • Quadrotor unmanned aerial vehicles (UAVs) require advanced control for precise trajectory tracking.
    • Standard nonlinear model-predictive control (NMPC) faces computational challenges for real-time UAV applications.

    Purpose of the Study:

    • To develop an efficient NMPC scheme for quadrotor UAV trajectory tracking.
    • To reduce the computational complexity of NMPC while ensuring closed-loop stability.

    Main Methods:

    • Augmenting the desired trajectory to a reference dynamical system to fit the standard NMPC framework.
    • Developing an improved continuation/generalized minimal residual (C/GMRES) algorithm by relaxing inequality constraints with a penalty term.
    • Introducing a contraction constraint to guarantee closed-loop system stability.

    Main Results:

    • The proposed efficient-NMPC algorithm achieves acceptable control performance with reduced computational complexity.
    • Numerical convergence of C/GMRES solutions and closed-loop stability of efficient-NMPC are theoretically analyzed under input constraints.
    • Simulations (numerical, SIL) and real-time experiments validate the effectiveness of the C/GMRES algorithm and efficient-NMPC scheme.

    Conclusions:

    • The developed efficient-NMPC scheme effectively addresses the computational burden of trajectory tracking for quadrotor UAVs.
    • The combination of the improved C/GMRES algorithm and contraction constraint ensures robust control performance and system stability.