This article presents a guidance path planning and real-time control solution for power-descent landing of an ovoid-shape vehicle in simulation. Given a vehicle initially in free-fall state at a location in mid-air site, the proposed solution will guide and control it by the thrusters to land it at a target location on the ground. The solution consists of an offline guidance path planning step and a real-time control step. It uses the result of a convexified guidance path planning to tune an Inverse Kinematics coupled PID controller with feedforward routes. In a Bullet Physics [1] based simulation environment, experiments were conducted to show good alignment with guidance, i.e. averaged Root Mean Square Error of position, velocity and attitude-in-quaternion are within, and respectively during a divert up to 120 m horizontally and 100 m vertically, against several disturbances including reducing mass, fluctuating center of mass, scheduling uncertainty and simulated wind, while the simulation frame rate is kept at around 60 fps for convenient real-time interaction.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Fuel-Optimal Landing Guidance and Inverse Kinematics Coupled PID Control Solution for Power-Descent Vertical Landing in Simulation


    Beteiligte:
    Yongfeng Lu (Autor:in) / Zejian Chen (Autor:in)


    Erscheinungsdatum :

    2022



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Fuel Efficient Powered Descent Guidance for Mars Landing

    Topcu, Ufuk / Casoliva, Jordi / Mease, Kenneth | AIAA | 2005


    Minimum-Fuel Powered Descent Guidance for Mars Landing

    Chengchao, Bai / Jifeng, Guo / Hongxing, Zheng | IEEE | 2018



    LEARNING-BASED OPTIMAL CONTROL FOR PLANETARY ENTRY, POWERED DESCENT AND LANDING GUIDANCE

    You, Sixiong / Wan, Changhuang / Dai, Ran et al. | TIBKAT | 2020


    Learning-based Optimal Control for Planetary Entry, Powered Descent and Landing Guidance

    You, Sixiong / Wan, Changhuang / Dai, Ran et al. | AIAA | 2020