Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance


    Contributors:
    K. Grantham (author)

    Publication date :

    2003


    Size :

    7 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English