A recently proposed hybrid-neutral-network and fuzzy-logic-control architecture is applied to a fuzzy logic controller developed for attitude control of the Space Shuttle. A model using reinforcement learning and learning from past experience for fine-tuning its knowledge base is proposed. Two main components of this approximate reasoning-based intelligent control (ARIC) model - an action-state evaluation network and action selection network are described as well as the Space Shuttle attitude controller. An ARIC model for the controller is presented, and it is noted that the input layer in each network includes three nodes representing the angle error, angle error rate, and bias node. Preliminary results indicate that the controller can hold the pitch rate within its desired deadband and starts to use the jets at about 500 sec in the run.


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    Title :

    Approximate reasoning-based learning and control for proximity operations and docking in space


    Contributors:

    Conference:

    AIAA Guidance, Navigation and Control Conference ; 1991 ; New Orleans, LA, United States


    Publication date :

    1991-01-01


    Type of media :

    Conference paper


    Type of material :

    No indication


    Language :

    English


    Keywords :



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