Perching maneuvers are a kind of landing mode of unmanned aerial vehicles (UAVs) mimicking the movement of birds, which involve highly dynamics nonlinear and a series of constraints on the states and inputs. To address these challenges, a novel integrated strategy of trajectory optimization and control design for UAV perching maneuvers is proposed. Unlike the previous approaches that necessitate a pre-determined reference trajectory, the data-driven iterative learning model predictive control (ILPMC) strategy does not rely on a prescribed reference trajectory. Instead, it iteratively improves the control performance based on historical data, starting with a single feasible initial trajectory. This approach is adaptable to varying scene settings, including different landing point positions, and is capable of handling model uncertainty and initial deviation. The simulation results show that the control strategy proposed in this paper can solve the optimal problem of perching maneuvers with aerodynamic uncertainty and initial error.


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    Titel :

    Control Design for UAV Perching Maneuvers Based on Iterative Learning MPC


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Deng, Yimin (Herausgeber:in) / Zijun, Zhou (Autor:in) / Zhen, He (Autor:in) / Feng, Yu (Autor:in) / Siying, Lin (Autor:in)

    Kongress:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Erscheinungsdatum :

    11.03.2025


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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