A robust trajectory optimization approach for guidance algorithm gain selection for powered descent and landing is developed. This approach uses a genetic algorithm to determine optimal guidance algorithm parameters while incorporating uncertainty information from linear covariance analysis. The optimal guidance algorithm parameters are determined while accounting for environment, navigation, and vehicle property uncertainty and sensor suite fidelity. As a demonstration of this method, the optimal gains for the fractional polynomial powered descent guidance are found for the braking phase of a robotic lunar landing mission. Scenarios with differing sensor suites and sensor qualities are considered, with objective functions to minimize variability in propellant usage or terminal position. Results show that the optimal guidance algorithm gains for a given trajectory differ based on the sensor suite, and optimal guidance algorithm gains may result in up to 20% performance improvements over the baseline in propellant usage and landed accuracy.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Robust Trajectory Optimization for Guided Powered Descent and Landing


    Contributors:

    Conference:

    AAS/AIAA Astrodynamics Specialists Conference ; 2022 ; Charlotte, NC, US


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English


    Keywords :


    Robust Trajectory Optimization for Guided Powered Descent and Landing

    G. E. Calkins / D. C. Woffinden / Z. R. Putnam | NTIS | 2022



    Multi-Objective Robust Trajectory Design for Powered Descent and Landing

    Calkins, Grace E. / Putnam, Zachary R. / Woffinden, David C. | AIAA | 2024


    Multi-Objective Robust Trajectory Design for Powered Descent and Landing

    Calkins, Grace E. / Putnam, Zachary R. / Woffinden, David | AIAA | 2024