Transport, rescue, search, surveillance, and disaster relief tasks are some applications that can be developed with unmanned aerial vehicles (UAVs), where accurate trajectory tracking is a crucial property to operate in a cluttered environment or under uncertainties. However, this is challenging due to high nonlinear dynamics, system constraints, and uncertainties presented in cluttered environments. Hence, uncertainties in the form of unmodeled dynamics, aerodynamic effects, and external disturbances such as wind can produce unstable feedback control schemes, introducing significant positional tracking errors. This work presents a detailed comparative study between controllers such as nonlinear model predictive control (NMPC) and non-predictive baseline feedback controllers, with particular attention to tracking accuracy and computational efficiency. The development of the non-predictive feedback controller schemes was divided into inverse differential kinematics and inverse dynamic compensation of the aerial vehicle. The design of the two controllers uses the mathematical model of UAV and nonlinear control theory, guaranteeing a low computational cost and an asymptotically stable algorithm. The NMPC formulation was developed considering system constraints, where the simplified dynamic model was included; additionally, the boundaries in control actions and a candidate Lyapunov function guarantees the stability of the control structure. Finally, this work uses the commercial simulator DJI brand and DJI Matrice 100 UAV in real-world experiments, where the NMPC shows a reduction in tracking error, indicating the advantages of this formulation.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Comparative Study between NMPC and Baseline Feedback Controllers for UAV Trajectory Tracking


    Beteiligte:


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    NMPC trajectory planner for urban autonomous driving

    Micheli, F. / Bersani, M. / Arrigoni, S. et al. | Taylor & Francis Verlag | 2023

    Freier Zugriff

    An improved NMPC-NDOB scheme for trajectory tracking of unmanned surface vessel

    Liu, Zhilin / Yuan, Shouzheng / Zheng, Linhe et al. | SAGE Publications | 2022


    NMPC and genetic algorithm based approach for trajectory tracking and collision avoidance of UAVs

    DE FILIPPIS, LUCA / GUGLIERI, GIORGIO | BASE | 2013

    Freier Zugriff

    Trajectory planning and control for autonomous vehicles: a “fast” data-aided NMPC approach

    Mattia Boggio / Carlo Novara / Michele Taragna | BASE | 2023

    Freier Zugriff

    Comparison of trajectory tracking controllers for emergency situations

    Hes, Daniel / Althoff, Matthias / Sattel, Thomas | IEEE | 2013