The attitude control problem of the quadrotor in the presence of disturbance and model uncertainty is studied in this paper. Firstly, a first-order filter is applied to generate the desired derivate of the reference signal. Then, a model-free adaptive attitude controller is designed for the condition that model parameters are not available. The discrete equation of the angular velocity is obtained by using the compact form dynamic linearization method, and the cascade controller is established based on the continuous kinematics and discrete dynamics. In addition, tent map-based pigeon-inspired optimization is designed to optimize the parameters of the filter and controller. Compared with original pigeon-inspired optimization, the premature problem can be effectively contained. Finally, the simulation results demonstrate the feasibility of the model-free attitude controller and the advantages of the Tent map-based pigeon-inspired optimization.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Consider for Best Student Paper Award Model-Free Control for Quadrotor Attitude via Tent Map-Based Pigeon-Inspired Optimization


    Beteiligte:
    Yuan, Yang (Autor:in) / Duan, Haibin (Autor:in) / Wei, Chen (Autor:in)


    Erscheinungsdatum :

    2023-06-06


    Format / Umfang :

    1754800 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Quantum-behaved Pigeon-inspired optimization Algorithm based on Particle-best Mutation

    Xiao, Bingsong / Wang, Rui / Xu, Yang et al. | IEEE | 2019


    Autonomous trajectory tracking of a quadrotor UAV using ANFIS controller based on Gaussian pigeon-inspired optimization

    Selma, Boumediene / Chouraqui, Samira / Selma, Belkacem et al. | Springer Verlag | 2021



    Chattering free sliding adaptive attitude control for quadrotor

    Zhi Li, / Xin Ma, / Zhigang Xu, et al. | IEEE | 2016